Open to Collaborate | Storyteller | Lead Data Engineer@Wavicle| Linkedin Top Voice 2024 | Globant | Linkedin Learning Instructor | 2xGCP & AWS Certified | LICAP'2022

Data Engineer with 7+ years of experience working on data engineering, analysis, and modelling projects for various domains, such as banking, healthcare, sales, and e-commerce. I leverage my strong statistical and analytical ability to explore business ideas and provide innovative solutions. 

Skilled with technologies and strong intellect with enthusiasm as an avid learner with challenging opportunities. I’m working as a LinkedIn Learning Instructor to bring change as a data evangelist.

Post Graduated in Master of Technology in Data Science and Engineering from Birla Institute of Technology and Science, Pilani in 2023, where I learned advanced concepts and techniques in data science and engineering, machine learning, cloud computing, and visualization. 

I hold multiple certifications in GCP, AWS, and Azure, and I enjoy working with cloud technologies and big data platforms.

I am passionate about data science and women empowerment, and I have been recognized as the 
- Linkedin Top Voice 2025, 2024
- Data Science and Women Empowerment Thought Leader by Global AI Hub. 

I am also a proud participant of the LinkedIn Creator Accelerator Program 2022, where I share my insights and experiences with the data science community. 

I am open to collaborate and share ideas, or explore various opportunities in the data field.

Pooja Jain

The Great Orange Processing Wars: ETL vs ELT vs EtLT

Three factories. Three philosophies. 
One truth most data engineers miss.

𝗘𝗧𝗟 — The Juice Bar
Squeeze before you store. Quality-first, schema-first, compliance-first.

Works perfectly until your business logic changes — then you re-run everything.
Best for: fraud detection, classical ML, regulated industries.
Hidden cost: you can't un-squeeze juice. Raw fidelity is gone forever.
These need strict compliance and clean, pre-processed data before loading.

𝗘𝗟𝗧 — The Warehouse
Store everything raw. Transform strictly on demand. Pure flexibility.

Works perfectly until nobody knows what's in Aisle 47 anymore.
Best for: ad-hoc analytics, LLM training data, fast-moving startups.
Hidden cost: "we'll clean it later" becomes never. Swamps, not lakes.
Here, raw data is loaded first, then transformed as needed—great for massive, unstructured, fast-changing datasets.

 𝗘𝘁𝗟𝗧 — The Gourmet Factory
The lowercase t changes everything.

E — Extract faithfully
t —  Fix only what must be fixed early (PII masking, dedup, type casting)
L — Load into columnar storage
T — Transform richly at query time, per use case

The small t handles what belongs at ingestion: compliance, deduplication, partitioning.
The big T handles what belongs at consumption: business logic, ML features, aggregations.
Best for: RAG pipelines, LLM training, GDPR systems, real-time personalization.
Used, when different data types need different strategies.

The real choice isn’t just technical; it’s about timing.
→ ETL commits early.
→ ELT commits late.
→ EtLT commits minimally early — but maximally late.

That tradeoff shows up everywhere in system design.
Eager vs lazy. 
Compiled vs interpreted. 
Normalized vs denormalized

💡 The Modern Data Engineering Playbook
Stop asking: "ETL or ELT?"
Instead, start asking: "What fuels my AI use case?"
🍊 Traditional ML? → Serve up that perfectly squeezed juice (ETL)
🥤 Exploratory AI? → Keep the whole fruits on hand, blend them later (ELT)
⚡ AI at Scale? → Prepare smartly, create a variety (EtLT)

🎯 The Truth Bomb
AI is like the promised land—bringing automation, intelligence, and scalability.
But without the right pipeline? It’s just chaos.
The best data engineers don’t play favorites.
They choose the right tool for the right need.

What’s your philosophy on data pipelines? 👇
Share it in the comments!

🔍 𝗔–𝗭 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: 𝗬𝗼𝘂𝗿 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗠𝗮𝗽!

In a world of limited time but unlimited tools, 
Data Engineers are expected to master everything — from ingestion to orchestration, from storage to serving, and from DevOps to AI.

The demand is booming. 
The ecosystem is evolving.
And the lifecycle? 
It’s massive.

📚 Sharing the A–Z snapshot of the tools & frameworks every data engineer should know:-
 
 𝗔 – Airflow, Avro, AWS
 𝗕 – BigQuery, Beam, Bigtable
 𝗖 – Cassandra, Cloudera, Couchbase
 𝗗 – Databricks, dbt, Dagster
 𝗘 – Elasticsearch
 𝗙 – Flink, Fivetran, Fault Tolerance
 𝗚 – Git, Glue, Grafana
 𝗛 – Hadoop, Hive, Hudi
 𝗜 – Iceberg, InfluxDB
 𝗝 – Jenkins, Jupyter, JSON
 𝗞 – Kafka, Kubernetes, Kestra
 𝗟 – Looker, Lakehouse, Luigi
 𝗠 – MongoDB, Metabase, Matillion
 𝗡 – NiFi, Neo4j
 𝗢 – OLTP/OLAP, Observability
 𝗣  – Python, PostgreSQL, Prefect
 𝗤 – Query Optimization, Qlikview
 𝗥 – Redshift, RabbitMQ, Redis
 𝗦 – Spark, Snowflake, SQL
 𝗧 – Terraform, Tableau, Talend
 𝗨 – Unix, Unity Catalog
 𝗩 – Versioning, Vertica
 𝗪 – Workflow Management, Warehousing
 𝗫 – XML, XGBoost
 𝗬 – YAML, YARN
 𝗭 – Z-Ordering, ZooKeeper

📍 Bonus: Don’t miss emerging trends like Data Mesh, LLMs, and Generative AI reshaping the future of data engineering.

👉 What tools or concepts would you add to this list?
Drop your favorite in the comments and let’s build the most complete A–Z cheat sheet together!

#Data #Engineering #ETL #BigData #Cloud #AI

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The Iceberg nobody shows you about Data Engineering!

Data engineers don't surface level things like,
Spark and Kafka and Snowflake
$150k salary dreams
Building ML pipelines
Modern data stack vibes

Here’s how the life of a ℝ𝔼𝔸𝕃 𝔻𝔸𝕋𝔸 𝔼ℕ𝔾𝕀ℕ𝔼𝔼ℝ looks like  😮‍💨
(The brutal truth nobody tells you)

1️⃣ 𝗖𝗮𝗿𝗲𝗲𝗿 𝗖𝗵𝗮𝗻𝗴𝗲𝗿: “I’ll master Spark, Kafka, and Snowflake in 3 months and land a $150K job!”
2️⃣ 𝗝𝗼𝗯 𝗛𝘂𝗻𝘁𝗲𝗿: “Why do they want 5 years experience for an ‘entry-level’ position? And what’s a ‘take-home coding challenge’?”
3️⃣ 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗖𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲: “I spent 40 hours building their pipeline assignment. They ghosted me after the final round.”
4️⃣ 𝗝𝘂𝗻𝗶𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: “The production data is nothing like the tutorial datasets. Why is everything broken and undocumented?”
5️⃣ 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: “I’m not building ML models. I’m fixing CSV parsing errors and explaining why dashboards show different numbers.”
6️⃣ 𝗦𝗲𝗻𝗶𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: “60% of my time is meetings about data governance. The other 40% is arguing with stakeholders about SLAs.”
7️⃣ 𝗟𝗲𝗮𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: “The pipeline that worked for 2 years suddenly failed. Nobody knows why. The person who built it left 6 months ago.”
8️⃣ 𝗦𝘁𝗮𝗳𝗳 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: “I spend more time in Slack explaining why data is 2 hours late than actually engineering. Also, our AWS bill is $50K this month.”
9️⃣ 𝗗𝗮𝘁𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁: “I designed the perfect architecture. Nobody followed it. Everything runs on Excel sheets shared via email. I’ve accepted this.”
🔟 𝗖𝗧𝗢 (𝗳𝗼𝗿𝗺𝗲𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿): “We need to be more data-driven. Also, can you build this by Friday? And make it real-time. Budget is $500.”

The real job description: 
• 20% building cool stuff 
• 80% debugging, meetings, and existential crisis

What they don’t tell you:
→ You’ll become a CSV therapist
→ “Data quality” means fixing the same broken pipeline every week
→ The hardest part isn’t the tech—it’s managing expectations
→ It's about business chaos and technical constraints
→ Data quality isn't a tool. It's an ownership issue
→ Most failures aren't technical. They're communicational
→ Scalability is easy to achieve. Designing and enforcing are two different things
→ The more senior you get, the less you're involved

The most challenging production bug is about stakeholder misalignment.
"Real-time" data usually means we didn't plan for this. 

Data engineers, put differently, are part therapists, detectives, and negotiators.

And despite the 2AM alerts, data pipeline issues and never ending slack threads —

It’s a privilege because in the end, 
the pipeline runs, 
the dashboard matches,
… And leadership finally trusts the numbers. Pure magic ✨

But here’s the thing… Despite all this, we still love what we do.

Do you remember, when did you automate your first pipeline?

Dear Data Engineers — meet your new coworker. It's not human.
Monday, 9:47 AM. Analyst pings: "orders_final is missing 10K rows."

You: Open Airflow. Query source. Compare counts. Find schema drift. Manual backfill. 45 minutes. Annoyed.
The Agent: Queries metadata → checks logs → detects schema drift → generates fix → asks "Run this?" 90 seconds. Done.

Here's the analogy that makes it click:

AI Agents = Self-modifying Airflow DAGs
𝖸𝗈𝗎 𝗁𝖺𝗋𝖽𝖼𝗈𝖽𝖾: 𝖾𝗑𝗍𝗋𝖺𝖼𝗍() → 𝗍𝗋𝖺𝗇𝗌𝖿𝗈𝗋𝗆() → 𝗅𝗈𝖺𝖽()

Agents do:
𝗐𝗁𝗂𝗅𝖾 𝗇𝗈𝗍 𝗌𝗈𝗅𝗏𝖾𝖽:
  𝗍𝗁𝗂𝗇𝗄() → 𝗉𝗂𝖼𝗄_𝗍𝗈𝗈𝗅() → 𝖾𝗑𝖾𝖼𝗎𝗍𝖾() → 𝖾𝗏𝖺𝗅𝗎𝖺𝗍𝖾()

Your DAG just became sentient and stopped asking for help.

The good news? You already have 80% of the skills.

Your Python scripts ARE the tools. You just need to make them agent-callable — add clear docstrings, wrap them as functions, expose them to the loop. 

The real shift isn't technical. It's mental.

Before: you were the pipeline. 
After: you design the system that runs the pipeline.
Before: 2 AM Slack pings wake you up. 
After: the agent handles it. You review the decision over morning coffee.

You're not being replaced. You're being scaled.
One DE + three agents = a five-person team's output.
Agents handle 2 AM fires. You architect system!

Working with AI Agents? Here are a few rules I always find myself returning to.

Do's:
✅ Debug it weekly? Automate it. Agents thrive on tools, not just shared knowledge.
✅ Document as if the reader knows nothing. Trust me, the agent definitely doesn’t.
✅ Record every decision made by agents. When things go wrong, you’ll want a clear trail.

Don'ts:
❌ Granting write access on Day 1. Start with read-only. Trust is something you build over time.
❌ Skipping human checkpoints on critical operations. Always gate anything that changes data.
❌ Assuming agents understand your context. They don’t. Make it crystal clear.

What’s the takeaway?
Agents reflect your habits—both the good and the bad.
So, build cleanly. Document thoroughly. Keep communication open where it counts.

You were building systems for humans,
Now build them for Agents.
This post is inspired by Vu Trinh's article on AI Agents. Find the link below. 👇

🌊 A fish thinks water needs it. Water thinks it needs the fish.

Then the net drops.

The fish gets captured. 
The water? It simply flows through.

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝘄𝗵𝗮𝘁’𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗶𝗻 𝗔𝗜 𝘁𝗼𝗱𝗮𝘆.

Raw data thinks AI can't survive without it. 
AI systems think any data is worth training on.

Then comes the real test — production.

And suddenly:

🐟 Ungovernated data gets 𝘁𝗿𝗮𝗽𝗽𝗲𝗱 in compliance failures
🐟 Unobserved pipelines get 𝗰𝗮𝘂𝗴𝗵𝘁 in silent corruption
🐟 Poor quality features get 𝗻𝗲𝘁𝘁𝗲𝗱 by model drift
But the water — the Governance framework — just flows right through.

It was never enslaved by bad data.

Here's the truth nobody tells you:
Most organizations treat data like fish — abundant, assumed, and disposable.

The ones building real AI treat data like whales.

Whales can't be netted…
They're too governed, too observable, too quality-conscious to be trapped by broken systems.

The 3 pillars that separate fish from whales:

⚙️ 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 = The ocean itself (Ownership, lineage, policy — the environment data lives in)

👁️ 𝗗𝗮𝘁𝗮 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 = Knowing where the whale is (Freshness, volume, schema, distribution — real-time awareness)

✅ 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 = The whale's immune system (Completeness, accuracy, consistency — what makes it survive production)

The ocean doesn’t mourn the fish lost to the net.
It only protects what chose to become sovereign.
Your data can survive capture — or it can command the ocean.

𝐴𝐼 𝑑𝑜𝑒𝑠𝑛’𝑡 𝑓𝑎𝑖𝑙 𝑏𝑒𝑐𝑎𝑢𝑠𝑒 𝑜𝑓 𝑏𝑎𝑑 𝑚𝑜𝑑𝑒𝑙𝑠. 𝐴𝐼 𝑓𝑎𝑖𝑙𝑠 𝑏𝑒𝑐𝑎𝑢𝑠𝑒 𝑜𝑟𝑔𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑠 𝑓𝑖𝑠ℎ 𝑓𝑜𝑟 𝑑𝑎𝑡𝑎 𝑖𝑛𝑠𝑡𝑒𝑎𝑑 𝑜𝑓 𝑟𝑎𝑖𝑠𝑖𝑛𝑔 𝑤ℎ𝑎𝑙𝑒𝑠.

💬 Are you fishing for data or raising whales?
#AI #Data

Communication is the key - From raw data to profitable outcomes!

It’s the TRUST,
With CLEAN outcomes,
To deliver solutions with IMPACT!

…. That helps you refine — Raw data into reliable solutions to deliver PROFIT.

Most data and AI teams are sitting on a treasure trove — and they might not even realize it.  

What separates a $1M budget line from a $10M+ profit powerhouse? 
It’s not just your algorithm or your tech stack. It’s all about how you navigate through these five stages:  

1️⃣ RAW DATA = The Ore It’s messy, unstructured, and often trapped in databases that no one fully trusts. Every company has it, but very few know how to refine it.  

2️⃣ SORTED = Refined Ingots Now we’re talking! Cleaned, labeled, and structured — think Metal, Copper, Gold, Silver. It’s got form, but just having form doesn’t mean it’s market-ready.  

3️⃣ ENGINEERED AI = The Component This is where your machine learning models, logic layers, and automation triggers come into play. Your engineers have crafted some impressive parts, but they’re still just that — parts. 

4️⃣ INTEGRATED SOLUTION = The Product Costs drop ↓ and revenues rise ↑. Now you’ve got something the market can actually USE. This is where the $10M+ potential lies.  

5️⃣ EXPLAINED STORY = The ROI “We transformed from a $1M cost center into a $10M profit machine.” This is the kind of statement that gets budgets approved, deals sealed, and careers launched.  

Here’s the hard truth:  
Most engineers stop at Stage 3, 
while many managers only get to Stage 4. 

The true winners — the leaders who really excel — are the ones who thrive at Stage 5.  

Keep this in mind:
Communication transforms beans into drinks.
Trust transforms drinks into habits.
Impact transforms habits into profits.

Stop building houses. 
Start constructing Profit Factories.  
That’s the 10X leap.  

♻️ Save this if you’re leading a #data or #AI team. 
💬 Which stage is your team currently stuck in? Let us know in the comments!

When DATA GOVERNANCE comes as an afterthought, you're literally screwed!

Agree?
Ignoring foundations that builds data trust can be painful.

Someone once asked me — Pooja, do you really think a simple data pipeline could cost such a huge bill?

Absolutely, it can. 
Data solution that is easy to represent.
But, here's the common pitfall many teams encounter:

→ The pipeline looks pristine on a whiteboard
→ It’s easy to draw. Easy to demo.
→ But scaling it? That’s a whole different ballgame. Trusting it? Even harder.

Here's what nobody tells you right off the systems:

📌 A sleek diagram ≠ a dependable data system
📌 Quick data movement ≠ accurate data movement
📌 More pipelines without governance = more confusion, not more clarity

What does DATA GOVERNANCE truly offers the teams?

✅ Everyone knows "who’s responsible" for what data
✅ No more debates over "which version is the right one?"
✅ Teams spend less time troubleshooting — and more time making "decisions"
✅ Your data builds "trust". Both internally and externally.

What’s the real price of skipping it?

It’s not just about the money.
It’s about team frustration.
It’s about flawed decisions.
And it’s about data that no one trusts.

Governance isn’t just a tech problem.
It’s a people + process + discipline problem.

Build the foundations before you start building floors.

Start small, but make it intentional.
Be honest, don't let a chat with the CFO at 9 AM cause chaos.
The $1150K surprise bill on a Monday morning can be thrilling over the week 😬

Here’s are some amazing folks to follow and learn more on Data and AI governance —

What's your perspective data leaders? 
George Firican 
John Wernfeldt 
Clare Kitching 
Charlotte Ledoux  
Dylan Anderson  
Piotr Czarnas 

What’s the one governance gap your team keeps revisiting?
Share it below 👇

Security Data Engineer @Just Eat Takeaway| Ex-bigspark, PayU| Mentor @ Learnbay| Women Health | Mental Health

I bridge two worlds: data engineering and developer advocacy.
I’ve spent years building secure and scalable systems at companies like Ford, Just Eat, and PayU, and I’ve learned that the real value comes from sharing those lessons with others. That’s why I use my LinkedIn platform to connect with CTOs, founders, and dev teams, making complex topics simple and actionable.

What sets me apart is that I’m not just speaking about theory. My work is rooted in hands-on engineering, which means the stories and insights I share are tested in real-world environments.

Alongside my engineering role, I create content and conversations for developers, founders, and technical leaders—on LinkedIn, in podcasts, and at events. My focus remains the same: to make complex technical topics clear, relevant, and actionable.

I also collaborate with brands, startups, and event organisers who want authentic voices to connect with developers. Whether it’s representing a company as an advocate, creating thought leadership content, or speaking at industry events, I aim to bring credibility and trust to every partnership.

Looking ahead, I am growing my presence as a tech evangelist and speaker in the B2B and developer-first space—building meaningful connections with leaders and communities that share the same commitment to clarity, trust, and innovation.

If you are a brand, founder, or event organiser seeking a credible and authentic voice at the intersection of engineering and advocacy, I would be glad to connect.

Disha Mukherjee

Everyone is rushing to become AI-first.
But most teams forget one thing.
AI sits on top of data infrastructure.

And if that foundation is weak, everything above it collapses.
From a data engineer’s perspective, the real order is:

Data → Pipelines → Quality → Governance → AI
Not the other way around.

When companies go AI-first without fixing their data layer, they face:
• Broken pipelines
• Poor data quality
• Hallucinated insights
• Unreliable models

It’s like building a luxury building on rotting pillars.

The companies winning with AI today did something different.

They went Data First.

They invested in:
• Reliable pipelines
• Strong data modeling
• Observability
• Data governance
• Scalable platforms

Only then did AI become powerful.

Because AI is only as good as the data beneath it.

For data engineers, this isn’t new.

We’ve always known:
Data infrastructure is the real foundation of AI.

💬 Curious to hear from other data engineers:

Do you think companies should go AI-first or Data-first?

If you found this helpful,
 🔁 Repost to help someone choose the right path.
 📌 Follow for practical insights on data careers and systems thinking.

📩 Subscribe to my Newsletter to get deep dives on data engineering, system design, and AI infrastructure - https://lnkd.in/eFPw_cd5

#DataEngineering #AI #DataPlatform #DataInfrastructure #DataQuality #MachineLearning

At some point in your data career, you realize something uncomfortable:

Being busy is easy.
Being impactful is rare.

I’ve seen teams celebrate:

Shipping 12 dashboards in a quarter.
Migrating to a new data stack.
Closing every Jira ticket in the sprint.
Deploying yet another model to production.

On paper, it looks impressive.

But then you ask one simple question:

“What changed because of this?”

Silence.

No decision improved.
No cost reduced.
No revenue influenced.
No behavior shifted.

That’s when it clicks.

Data work has two layers:

Layer 1 → Build artifacts.
Layer 2 → Change outcomes.

Most professionals optimize for Layer 1.
The best ones design for Layer 2.

A dashboard isn’t success.
A better decision is.

A model isn’t impact.
Adoption is.

A pipeline isn’t value.
Reliability that unlocks trust is.

If you want to become essential — not just employed — start measuring differently:

• Time-to-decision
• Decision quality
• Adoption rate
• Business movement
• Trust in data

Outputs keep you busy.
Outcomes build influence.

So here’s the real question:

When your work ships…
what actually moves?

Curious what’s the one metric you personally use to define success in data?

📩 Subscribe to my Newsletter to get deep dives on data engineering, system design, and AI infrastructure - https://lnkd.in/eFPw_cd5

#DataEngineering #DataLeadership #BusinessImpact #AnalyticsStrategy #DataDriven #TechCareers

I recently finished reading 𝐏𝐲𝐭𝐡𝐨𝐧 𝐈𝐥𝐥𝐮𝐬𝐭𝐫𝐚𝐭𝐞𝐝 by Maaike van Putten and Imke van Putten and I wanted to share a few thoughts.

As a cat person, I’ll admit…

The illustrated format immediately caught my attention 🐾

You don’t often see technical books lean this intentionally into visual storytelling. It’s very different from the typical dense, 500-page programming manual.

And that’s exactly the point.

𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐨𝐨𝐤 𝐝𝐨𝐞𝐬 𝐰𝐞𝐥𝐥
It’s clearly designed for:
• Beginners
• Visual learners
• People who feel overwhelmed by traditional coding books

𝐈𝐭 𝐰𝐚𝐥𝐤𝐬 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 𝐬𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩:
→ Environment setup & terminal basics
→ Variables & data types
→ Control flow & loops
→ Collections
→ Functions
→ File handling
→ Object-oriented programming & inheritance
→ Debugging

The structure is sequential and intentional.

Each chapter builds on the previous one.
And importantly it includes exercises and quizzes.

Because learning to code isn’t about reading.

It’s about doing.
What I appreciated most
The tone is light.

But the technical explanations are accurate and aligned with modern Python 3 practices.

And debugging is treated as a skill in its own right not just a footnote.

That matters.

Most beginners struggle not because syntax is hard…
But because they don’t know how to think when something breaks.

𝐖𝐡𝐨 𝐭𝐡𝐢𝐬 𝐛𝐨𝐨𝐤 𝐢𝐬 (𝐚𝐧𝐝 𝐢𝐬𝐧’𝐭) 𝐟𝐨𝐫
This is not a book about:
• Production-scale engineering
• System design
• Data architecture

It’s a fundamentals book.

It helps someone build clarity.
Confidence.
And mental models.

And honestly, we underestimate how important that stage is.

Strong fundamentals create strong engineers.

If you’re new to programming  or you learn better visually than through long blocks of text  this is worth exploring:
🔗 https://lnkd.in/excTGBSj

#Python #Programming #DataEngineering #TechBooks #LearningToCode #WomenInTech

Powering the Future of Data Engineering with the Cloud ☁️

Modern data engineering isn’t just about pipelines anymore.

It’s about scalable systems, real-time insights, and reliable data platforms that support everything from analytics to AI.

Today, the big three cloud platforms — Azure, AWS, and Google Cloud — provide a rich ecosystem of tools that power the entire data lifecycle.

From data ingestion → processing → analytics → governance → visualization.

Here’s a simple way to think about how cloud platforms support data engineering.

1️⃣ Storage & Data Lakes

Every data platform starts with storage.

Azure → Azure Data Lake
AWS → Amazon S3
GCP → Cloud Storage

These services provide scalable, durable storage for structured and unstructured data.

2️⃣ Data Pipelines & Processing

Once data is stored, it needs to be moved and transformed.

Azure → Azure Data Factory
AWS → AWS Glue
GCP → Dataflow

These tools help engineers build ETL/ELT pipelines and automate data movement across systems.

3️⃣ Big Data & Analytics

This is where data becomes insight.

Azure → Azure Synapse Analytics
AWS → Amazon Redshift
GCP → BigQuery

They allow teams to run large-scale analytical queries across massive datasets.

4️⃣ Real-Time Data Processing

Businesses increasingly need insights as events happen.

Azure → Stream Analytics
AWS → Kinesis
GCP → Pub/Sub

These services enable real-time pipelines for streaming data.

5️⃣ Visualization & Business Intelligence

Finally, insights must be accessible.

Azure → Power BI
AWS → QuickSight
GCP → Looker

These tools transform raw data into interactive dashboards and business insights.

What’s fascinating is that while each cloud has different tools, the underlying data engineering patterns remain the same.

Storage → Processing → Analytics → Visualization.

The platform changes.
The engineering thinking stays consistent.

And that’s the real skill data engineers build.

If you're learning data engineering, start by understanding the architecture patterns first the tools will follow.

Which cloud platform do you work with the most: Azure, AWS, or GCP?

#DataEngineering #CloudComputing #DataArchitecture #BigData #Analytics #TechLearning

What a night at the 𝐀𝐥𝐥𝐢𝐞𝐬 𝐢𝐧 𝐖𝐨𝐦𝐞𝐧 𝐢𝐧 𝐃𝐚𝐭𝐚 & 𝐀𝐈 𝐞𝐯𝐞𝐧𝐭. 

The room was full, the conversations were thoughtful, and the energy from the community was amazing to see. 

A huge shoutout to the 128 people who attended and made the discussion so engaging.

One theme came up again and again during the panel:
↳ 𝐓𝐡𝐞𝐫𝐞 𝐢𝐬 𝐧𝐨 𝐬𝐢𝐧𝐠𝐥𝐞 𝐩𝐚𝐭𝐡 𝐢𝐧𝐭𝐨 𝐚 𝐜𝐚𝐫𝐞𝐞𝐫 𝐢𝐧 𝐝𝐚𝐭𝐚.
↳ Every speaker had a different journey.
↳ Some started technical.
↳ Others came from completely different fields.
↳ Yet they all built impactful careers in data.

A few journeys that really stayed with me:
• 𝐉𝐨𝐬𝐢𝐞 𝐖𝐢𝐥𝐥𝐢𝐚𝐦𝐬 shared a more linear path - starting as a DBA, moving into consulting, then BI, and eventually leading teams.
• 𝐇𝐚𝐳𝐚𝐥 𝐌𝐮𝐡𝐭𝐚𝐫 began with no coding background, gradually moved into technical roles, and now works as Senior Director of Product Analytics at Wise.
• 𝐋𝐚𝐮𝐫𝐚 𝐋𝐞𝐰𝐢𝐬 took a very unconventional route - marketing → archaeology → treasury → startups → bootcamp → Amazon (Alexa) → consulting → Monzo — always driven by a desire to create impact.
• 𝐋𝐢𝐧𝐚 𝐌𝐢𝐤𝐨𝐥𝐚𝐣𝐜𝐳𝐲𝐤 initially pursued medicine, but during a gap year discovered a love for numbers while working in operations at an insurance company. That curiosity eventually led her into data roles and leadership after reaching out to alumni for opportunities.

 Different backgrounds.
 Different starting points.
 But one common lesson.

𝐈𝐟 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬, 𝐲𝐨𝐮 𝐨𝐟𝐭𝐞𝐧 𝐡𝐚𝐯𝐞 𝐭𝐨 𝐚𝐬𝐤 𝐟𝐨𝐫 𝐭𝐡𝐞𝐦.
The worst possible answer is 𝐧𝐨.

But sometimes asking leads to a conversation, a connection, or an opportunity you didn’t expect.

Another powerful takeaway:
𝐅𝐢𝐧𝐝 𝐲𝐨𝐮𝐫 𝐚𝐥𝐥𝐢𝐞𝐬.
Careers rarely grow in isolation.
They grow through people who support you, advocate for you, and open doors you didn’t know existed.

A few insights from the discussion that really stuck with me:
▫️ Don’t wait until you feel 100% ready. Growth often comes from stepping into opportunities before you feel fully confident.
▫️ Leadership isn’t about knowing everything. It’s about facilitating the conversation and helping teams navigate complexity.
▫️ Clear communication is essential. Being able to explain technical work in a way that business stakeholders understand is a powerful skill.
▫️ Self-doubt is common. Even experienced leaders experience it - the key is not letting it hold you back.
▫️ Relationships matter. Many opportunities come from connections and conversations, not just applications.

A huge thank you to our excellent panel for sharing their experiences:
- Lina Mikolajczyk
- Laura Lewis, PhD
- Hazal Muhtar
- Josie W.

Events like this are a great reminder of how powerful community and allies are in shaping careers in data and AI.

Looking forward to the next one.

#WomenInData #WomenInTech #DataCommunity #WomenInLeadership #DataCareers 🚀

Most people think success is a straight line.

It’s not.

It looks more like this 👇

You start out excited.
Trying to figure things out.

Then reality hits.

You try different paths.
You hit roadblocks.
You fail… and then fail again.

And somewhere in between all that chaos,
you learn the most important things:

→ Showing up even when it’s hard
→ Staying consistent when results are slow
→ Pushing through doubt when nothing makes sense

Because career growth isn’t about avoiding failure.

It’s about learning how to keep going *despite it.*

The people who succeed aren’t the ones who never fail.

They’re the ones who don’t stop.

What stage are you in right now?

#CareerGrowth #Consistency #LearningJourney #Mindset #WomenInTech #BuildInPublic

Most people use AI tools randomly.
That’s the mistake.
Not all AI tools are built for the same job.

𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐬𝐢𝐦𝐩𝐥𝐞 𝐰𝐚𝐲 𝐭𝐨 𝐭𝐡𝐢𝐧𝐤 𝐚𝐛𝐨𝐮𝐭 𝐢𝐭 👇
👉 𝐂𝐥𝐚𝐮𝐝𝐞 𝐂𝐡𝐚𝐭
Best when you need *thinking + clarity*
– Writing
– Research
– Brainstorming
– Explaining concepts

You stay in control. AI supports you.

👉 𝐂𝐥𝐚𝐮𝐝𝐞 𝐂𝐨𝐝𝐞
Best when you need *execution in code*
– Refactoring projects
– Running tests
– Working across multiple files
– Automating dev workflows

You define the goal. AI executes.


👉 𝐂𝐨𝐰𝐨𝐫𝐤
Best when you need *execution beyond code*
– File organization
– Report generation
– Cross-app workflows
– Repetitive business tasks

You describe the task. AI delivers outputs.

💡𝐓𝐡𝐞 𝐬𝐡𝐢𝐟𝐭 𝐢𝐬 𝐬𝐢𝐦𝐩𝐥𝐞:

𝐅𝐫𝐨𝐦
“Which AI tool is best?”

𝐓𝐨
“Which AI tool fits this task?”

That’s where real productivity begins.

If you had to pick one you use the most right now… which would it be?

If you found this helpful,
 🔁 Repost to help someone choose the right path.
 📌 Follow for practical insights on data careers and systems thinking.

📩 Subscribe to my Newsletter to get deep dives on data engineering, system design, and AI infrastructure - https://lnkd.in/eFPw_cd5

#AI #Productivity  #DeveloperTools  #Automation  #TechTrends  #FutureOfWork Claude

Image credits to respective creator

1 in 10 women worldwide live with endometriosis.
That’s around 190 million women.

Many of them are sitting in meetings right now.
Joining stand-ups.
Leading teams.
Writing code.
Presenting to clients.

And most people around them have no idea what they’re going through.

Endometriosis is not “just bad cramps.”

It can mean:
* Chronic pelvic pain
* Debilitating fatigue
* Brain fog
* Hormonal struggles
* Fertility challenges
* Years of delayed diagnosis

For many women, the pain can be so severe it feels impossible to stand — yet they still show up to work.

They smile through meetings.
Deliver deadlines.
Lead projects.

All while their body is fighting a battle that remains largely invisible.

And often, when they do speak up, they hear things like:
“Maybe it’s just stress.”
“It’s normal period pain.”
“Take a painkiller.”

But the truth is - it’s not normal.

It’s a serious condition that affects not just health, but careers, confidence, and everyday life.

So today, I want to give a shoutout to every woman living and working with endometriosis.

To the women pushing through the pain.
To the women advocating for their health.
To the women who refuse to let this condition define their potential.

Your resilience is extraordinary.

And to workplaces and leaders everywhere:

Let’s build cultures where women feel safe talking about health challenges.

Where empathy is not seen as weakness.

And where invisible illnesses are acknowledged, not dismissed.
Because strength isn’t just showing up when everything is easy.
Sometimes strength is simply showing up despite the pain.

To all the endometriosis warriors out there 
we see you, we respect you, and we stand with you.

❤️

#EndometriosisAwareness #WomenInTech #InvisibleIllness #WomenSupportingWomen #WorkplaceEmpathy #EndoWarrior

I once organised a meeting.
Set the agenda.
Prepared every talking point.
Sent the invite with clarity and intent.
And then, while sitting in that room, I felt it slip away.
Not abruptly. Not aggressively.
But quietly. Smoothly. Almost invisibly.
A small interruption here.
A subtle reframe there.
Until, before I even realised it, my ideas were moving around the room… just not in my voice anymore.
By the end, the conversation had shifted.
The ownership had shifted. And somehow, the meeting no longer felt like mine.
I was still there. Just no longer part of it.

That experience stayed with me. Not as bitterness, but as a question:

What would it have taken for that room to work differently?

I think it comes down to four small habits that cost nothing but intention:

1. Name the idea AND the person, out loud
When a leader summarises a discussion, they should say: "Priya raised this" or "that came from Disha." Not in the notes later. In the room, in the moment. Attribution spoken aloud is attribution that sticks.

2. Protect the unfinished thought
Create one space in every meeting where no one interrupts. Not to correct, not to build on, not to redirect. Just to let the thought land as it was meant to. Many of us never get that.

3. Write it down as it happens
A live shared doc with ideas and names beside them. It is surprisingly hard to take credit for something already in writing, next to someone else's name, in a document twelve people can see.

4. Assign someone to look back
One person per meeting whose job is simply to notice: "Did anything get lost?" And to bring it back. "I want to return to what she said earlier, because I don't think we finished with it."

None of these is radical.

They don't require a policy change or a training budget. They require a leader who is paying attention to more than just the loudest voices in the room.

The best leaders don't just open doors.

They notice who walked through, what they said, and make sure the room heard it too.

#IWD2026 #WomenAtWork #LinkedInNewsUK

Early in her career, Emma Chalwin, the chief marketing officer of Workday, shared an idea in a room full of male executives.

After the meeting, the feedback she received was not about the substance of her idea, but about her tone. She was told she was "too emotional". 

The next day, a male colleague repeated her idea – and was praised for it. 

That experience quieted her voice. She spoke less, until a new manager changed the dynamic. In a meeting months later, when Emma hesitated, that manager pushed the floor back to her: "Emma has a really excellent point of view," she said. "She's a subject matter expert in this area and I'd love for her to share her perspective."

Sometimes leadership is simply making sure the right voice is heard.

🤔 What's a constructive way to push back when people are labelled 'too emotional' at work and what can workplaces do to ensure women's ideas are heard?

Watch the video below and join the conversation.

#IWD2026 #WomenAtWork

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The Battle

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Disha Mukherjee

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taplio

The Battle

Pooja Jain

Disha Mukherjee

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Engagements

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Pooja Jain's LinkedIn Performance

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