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

How to Build a Real Data Engineering Project (From Zero)

Step 0: Start with the question
Write one sentence: what decision will this data support, who uses it, and how often it updates.
Example: “Track daily sales and flag unusual drops.”

If you can’t answer this, stop.

here’s the roadmap that actually works:
 
Step 1: Pull in messy data
Use raw inputs: CSVs, public APIs, or open datasets from Kaggle, data.gov, or GitHub.
The mess is the point.

Step 2: Read before you write
Open the data in Pandas or a notebook.
Check columns, types, missing values, and duplicates.
This step saves hours later.

Step 3: Clean and shape
Fix types, handle nulls, standardize values, and add derived fields.
Use SQL and Pandas. Tools matter less than clear logic.

Step 4: Store it simply
Pick one place as your source of truth: DuckDB, SQLite, or Postgres.
Design tables well. Don’t chase big platforms yet.

Step 5: Build the pipeline
Start with plain Python scripts.
Schedule with cron.
Only add Airflow or Prefect after it works end to end.

Step 6: Add checks
Row counts, null checks, freshness.
If data breaks silently, the project fails.

Step 7: Show the output
One dashboard or query layer using Metabase, Power BI, or Looker Studio.

Step 8: Ship it properly
GitHub repo, README, diagram, run steps, and assumptions.

Step 9: Improve
Add incremental loads, logging, schema handling, and faster queries.

The best part? Every single tool in this stack has a free tier.

If you are someone, who wants to explore your expertise and expand your data engineering portfolio, grab a look at these projects:

1. Build a simple ETL pipeline using Python and SQLite : Ankit Bansal - https://lnkd.in/gTdCV9aJ

2. End To End ETL Pipeline Using AirFlow And Astro : Krish Naik - https://lnkd.in/gusmJNfs

3. Data-Engineering-Project-with-HDFS-and-Kafka : Ahmet Furkan DEMIR -https://lnkd.in/gkw4wYSC

4. Develop a data warehouse solution using Snowflake, dbt : Shashank Mishra 🇮🇳https://lnkd.in/gf-c5TR7

5. Amazon Data Analysis Using Databricks and Snowflake: Kinzorize - https://lnkd.in/gUDiwYpQ

6. Uber Data Analytics using GCP: Darshil Parmar - https://lnkd.in/gqE-Y4HS

7. Pyspark Project : Alex Ioannides, PhD - https://lnkd.in/g-T_ZqXu

8. Apple Data Analysis(Spark) project : Ankur Ranjan - https://lnkd.in/gt9JAkRt

9. Airflow data pipeline with BigQuery, dbt Soda, and more - Marc Lamberti https://lnkd.in/g8xjqisu

10. AWS End to End Data Engineering : Yusuf Ganiyu - https://lnkd.in/gtj-tqWK

The engineers who move fast aren’t the ones with the best tools. 
They’re the ones who stop waiting for perfect and start building.

Learn, Earn or Leave

Loyalty is beautiful… until you realize  
it was never mutual.

Your company will replace you faster than you can say "team player"

The company?  
It’s just business.  
Treat it accordingly. 🛡️

Agree?

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Data pipelines are evolving from the "copy-paste" era to the "access-anywhere" era. Are you evolving with them?

At its core, data pipelines answers one thing -
How does data moves from "happened" to "decision made"?

Here's how the evolution looks like:
𝗘𝗧𝗟 (The Old Way) Extract → Transform → Load
 • Like preprocessing your groceries in the parking lot before bringing them inside
 • Great for strict governance, legacy systems
 • Slow. Rigid. Breaks on every schema change
 • Tools: Glue, Talend, NiFi

𝗘𝗟𝗧 (The Cloud Era) Load raw → Transform in warehouse
 • Load raw, transform in warehouse. Like, Dump groceries in fridge, prep later
 • Scales with cloud compute
 • Better, but still copying data everywhere
 • Tools: dbt, Snowflake, BigQuery

𝗖𝗗𝗖 (Real-Time Tax) Change Data Capture
 • Stream every database change via transaction logs
 • Captures inserts/updates/deletes from source. Near real‑time, low overhead
 • Perfect for fraud detection. Expensive for everything else
 • Tools: Debezium, AWS DMS

𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 (Over-Engineering Champion) 
 • Real‑time processing, no waiting. Just like Food truck serving as you arrive
 • Necessary for IoT, trading, Fraud and live dashboards
 • Overkill for your Monday morning dashboard
 • Tools: Kafka, Flink, Kinesis.

Enter 𝗭𝗲𝗿𝗼 𝗘𝗧𝗟: The "Why Are We Doing This?" Moment
The modern lakehouse game‑changer
One city library for everyone – no need to photocopy books.
Data lives in one place (lakehouse).
Query it directly with any engine. No copying.

🤔 How Modern Lakehouses Make This Real?

𝗢𝗽𝗲𝗻 𝗧𝗮𝗯𝗹𝗲 𝗙𝗼𝗿𝗺𝗮𝘁𝘀 (Iceberg, Hudi, Delta)
Think: Git for data. ACID transactions, time travel, schema evolution—on cheap object storage.

𝗦𝘁𝗼𝗿𝗮𝗴𝗲 ≠ 𝗖𝗼𝗺𝗽𝘂𝘁𝗲
Data sits in S3. Spark reads it. Snowflake queries it. Trino analyzes it. Same files. No copies. No vendor lock-in.

𝗡𝗮𝘁𝗶𝘃𝗲 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗼𝗿𝘀
AWS Aurora → S3? Managed replication.
No custom Glue jobs. No 3 AM debugging.

What Changes for You?

You stop:
- Writing extraction code for the 47th time
- Maintaining Airflow DAGs that "mysteriously fail on Wednesdays"
- Explaining why reports are 6 hours delayed
- Paying for storage in four different systems

You start:
- Designing smart partitioning strategies
- Building dbt models where transformation actually matters
- Focusing on data quality, not data plumbing
- Solving business problems instead of pipeline problems

The Real Talk?

Zero ETL isn't "no data engineering." 
It's engineering that matters.
Traditional ETL won't vanish overnight—too much legacy infrastructure. 

But for new projects? Start lakehouse-first:
- Store once in open formats
- Let compute engines come to data
- Create copies only when necessary

Think of it this way: 
You wouldn't photocopy a book every time someone wants to read a different chapter. 
So why copy your data every time a new team needs access?

Without governance, your data is a 10-lane highway with no traffic lights.

Everyone's driving. Nobody knows the rules. Accidents are inevitable.

Why does it Matters?
Think traffic system for organizational data.
 • Data flows smoothly, safely, smartly
 • Everyone knows their lane
 • Decisions made with confidence
 • Teams drive results, not fix messes

Without traffic lights, you don't get speed; you get gridlock and crashes.

🔑 Your Role as a Leader
Strategy & Oversight:
 → Champion governance (sponsorship + resources)
 → Set the tone for responsible data use
 → Hold leaders accountable for data quality
 → Not your job: Managing day-to-day data operations

👥 Who Does What ?(Simplifying the roles)
 • Chief Data Officer: Sets data strategy
 • Data Security Officer: Shields from risk
 • Head of Governance: Enforces data rules
 • Data Quality Manager: Validates accuracy

 → Data Owners: Accountable for specific datasets
 → Data Stewards: Maintain quality and access
 → Product Owners: Align data with user needs
 → Data Custodians: Handle storage and security

Here's to quick start and win early -
Reflect: Where do you rely on data today? Where do you trust it?
Clarify: Which outcomes matter most?
Identify: Who owns critical data decisions?

But, Why Now?
AI is scaling. Without governance, pilots stall and ROI evaporates.
Competition moves fast. Trusted data = speed advantage.
Regulations tighten. Fines and reputation hits are expensive.

𝘎𝘰𝘰𝘥 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘪𝘴𝘯'𝘵 𝘣𝘶𝘳𝘦𝘢𝘶𝘤𝘳𝘢𝘤𝘺—𝘪𝘵'𝘴 𝘵𝘩𝘦 𝘣𝘭𝘶𝘦𝘱𝘳𝘪𝘯𝘵 𝘧𝘰𝘳 𝘈𝘐 𝘵𝘩𝘢𝘵 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘥𝘦𝘭𝘪𝘷𝘦𝘳𝘴.

My final thoughts:
If 𝗱𝗮𝘁𝗮 is the fuel and 𝗔𝗜 is the engine,
𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 is the steering wheel.
Without it, speed means nothing.

Refer this to explore what Atlan says about Data Governance pillars in 2026 -https://lnkd.in/dAMTYJGy

Data Mesh for Data Engineers!
Most organizations still treat data like a giant centralized kitchen — slow, overloaded, and always backlogged.

Data Mesh flips that model.
Instead of one giant data team owning everything...
 → Domains own their data
 → Data is treated like a product
 → Platform teams provide self-serve infrastructure
 → Governance is federated, not chaotic

Think of multiple cafés, each owning its menu, ingredients, and customers 
That’s exactly how Data Mesh works
Decentralized, domain-owned, product-like data.

𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗮𝘁 𝗺𝗲𝗮𝗻?
Clear data contracts (no surprises downstream)
Built-in quality checks (catch issues before users do)
Versioned interfaces (breaking changes = major versions)
Metadata that actually helps people find your datasets

𝗗𝗼𝗲𝘀 𝘁𝗵𝗲 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗟𝗮𝘆𝗲𝗿 𝗠𝗮𝘁𝘁𝗲𝗿𝘀?
A mesh only works if engineers get:
Self-serve compute & storage
Standardized tooling (catalog, lineage, observability)
Guardrails, not gatekeepers
Without this? “Decentralization” just becomes distributed chaos.

𝗧𝗲𝗰𝗵 𝗶𝘀𝗻'𝘁 𝘁𝗵𝗲 𝗛𝗮𝗿𝗱 𝗽𝗮𝗿𝘁?
Data Mesh is an operating model shift:
Ownership moves to domains
Accountability follows ownership
Governance becomes federated and automated

It's less about new tools and more about changing how data responsibility works.
Done right, Data Mesh turns data engineering from a service desk into product engineering for data.

⚠️ Not a silver bullet — it needs strong governance + org-level change.
But when done right?
👉 It unlocks speed, scalability, and true data ownership.

💬Have you worked with Data Mesh? What actually worked vs what was just theory?

I watched a junior engineer spend 6 months building a Kafka-Spark-Kubernetes pipeline.
It crashed on day one in production.

Why? They never learned how to handle CSV files properly.

Here's what nobody tells you about data engineering:

The staircase exists for a reason. 
𝐒𝐤𝐢𝐩 𝐬𝐭𝐞𝐩𝐬, 𝐚𝐧𝐝 𝐲𝐨𝐮 𝐝𝐨𝐧’𝐭 𝐦𝐨𝐯𝐞 𝐟𝐚𝐬𝐭𝐞𝐫—𝐲𝐨𝐮 𝐟𝐚𝐥𝐥 𝐡𝐚𝐫𝐝𝐞𝐫.
You can't skip fundamentals without consequences.

Here’s the order that actually holds up in the real world:
  • SQL → then Snowflake
 • Python → then Databricks
 • Warehouse basics → then dbt
 • Files and formats → then data lakes
 • Relational databases → then NoSQL
 • Batch jobs → then streaming
 • Partitions → then performance work
 • Linux and networking → then cloud
 • Business context → then tuning queries
I’ve seen this play out again and again.

Two paths. Same destination:
Path 1: Climb every step—Databases, SQL, Python, ETL, Snowflake, Databricks, Kafka, Kubernetes. 6 months. $10K/month AWS bill. Constant debugging.
Path 2: Master fundamentals. Clean data + solid SQL. Week 1. $50/month. It just works.

😲 What's the difference?
Path 1 looks impressive on LinkedIn. 
Path 2 actually solves problems.

Complexity without fundamentals = expensive tech debt. 
Simplicity with strong basics = reliable systems.

𝘚𝘰𝘮𝘦𝘵𝘪𝘮𝘦𝘴 𝘵𝘩𝘦 𝘣𝘦𝘴𝘵 𝘥𝘢𝘵𝘢 𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮 𝘪𝘴 𝘵𝘩𝘦 𝘰𝘯𝘦 𝘺𝘰𝘶 𝘥𝘰𝘯'𝘵 𝘩𝘢𝘷𝘦 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥.

You wouldn't build a skyscraper on quicksand. So why are you building ML models on dirty data?

Data quality isn’t a tooling problem.
It’s a people problem.

You can buy every check, test, and dashboard on the market and still ship bad data if ownership is fuzzy.
Quality only works when everyone treats it as their job.

Here's the reality: Quality is a team sport. Whether you're:
Building pipelines (Data Engineers)
Analyzing trends (Data Analysts)
Training models (Data Scientists)
Enforcing standards (Data Stewards)
Making decisions (Business Leaders)

𝘌𝘷𝘦𝘳𝘺𝘰𝘯𝘦 𝘰𝘸𝘯𝘴 𝘥𝘢𝘵𝘢 𝘲𝘶𝘢𝘭𝘪𝘵𝘺. Not just the data team.

The Six Pillars That Hold Your Data Together:
Accuracy - Does it reflect reality?
Completeness - Are critical fields missing?
Consistency - Same format everywhere?
Timeliness - Fresh or stale?
Validity - Follows defined rules?
Uniqueness - Duplicates eliminated?

❗️𝗕𝗿𝗲𝗮𝗸 𝗼𝗻𝗲 𝗽𝗶𝗹𝗹𝗮𝗿, 𝗯𝗿𝗲𝗮𝗸 𝘁𝗵𝗲 𝗲𝗻𝘁𝗶𝗿𝗲 𝘀𝘆𝘀𝘁𝗲𝗺.

As a data engineer building governance always ensure that you track:
 • Time to Data - How long until new users can access data they need?
 • Data Trust Score - % of users who trust the data they use
 • Incident Response Time - How fast can you fix data quality issues?
 • Self-Service Adoption - Are users finding data themselves or asking you?
 • Compliance Coverage - % of sensitive data properly classified and protected
 • Metadata Completeness - How well documented is your data?

Bad data is expensive in quiet ways:
 Wrong numbers → wrong decisions
 Messy records → weak models
 Schema changes → broken pipelines

Netflix didn’t struggle because of algorithms.
They struggled when late or wrong data changed what people saw.
Data quality isn’t a project you “finish.”

It’s a habit you keep.
The teams that win aren’t drowning in data.
They’re clear on ownership, standards, and trust.

Monitor at every stage—source, pipeline, warehouse—not just at the end.

Why does this matters more than ever?
Data Quality Engineer roles: growing 42% annually
Chief Data Officer positions: $200K-$400K+
GDPR/CCPA compliance is non-negotiable
Gartner predicts 30% higher ROI with formal governance by 2026

Read more on Data Governance roles and responsibilities by Piotr Czarnas - https://lnkd.in/gy9jh_Cr
Refer this to explore what Atlan says about Data Governance pillars in 2026 -https://lnkd.in/dAMTYJGy

𝗚𝗮𝗿𝗯𝗮𝗴𝗲 𝗶𝗻 = 𝗚𝗮𝗿𝗯𝗮𝗴𝗲 𝗼𝘂𝘁. 
But here's what most people miss: Garbage tolerated = Trust destroyed."
What's your biggest data quality challenge? 👇

The GAP is real
Every common person is equally concerns about AI.
"Instagram/ Siri is hearing me and changing the recommendations"

One of my niece said this to me yesterday.
And.. I really felt like we're just on the different sides of the screen.

The irony?
We pull all-nighters worrying about data security...
They pull all-nighters worrying about data security.

What we're actually doing?
 → Building trust & safety frameworks
 → Implementing data privacy by design
 → Creating quality checks on quality checks
 → Making AI explainable and auditable

What people think we're doing?
 → Building Skynet
 → Stealing their job
 → Teaching Alexa to spy on them
 → Making everything too complicated on purpose
 
To get the real time understanding, these are some amazing blogs to read:
1. Seattle Data Guy - https://lnkd.in/g6kC-9wa
2. Data Engineering Things - https://lnkd.in/gg_hCpk7
3. DataExpert.io - https://lnkd.in/gxj-wAHD
4. Confessions of a Data Guy - https://lnkd.in/gXnAFW8g
5. Netflix Tech - https://lnkd.in/gUMZ9xpS
6. Engineering at Meta - https://lnkd.in/gg7k9pWh
7.  Spotify - https://lnkd.in/gJWShJKV
8. LinkedIn Engineering - https://lnkd.in/gw6aBvYn
9. Slack Engineering - https://slack.engineering/
10. Uber Engineering - https://lnkd.in/gyUdJcMb
11. PayPal Tech - https://lnkd.in/gdmiErJY
12. Airbnb Engineering - https://lnkd.in/gQVYUeJY
13. Databricks - https://lnkd.in/gD24FB63
14. Canva Engineering - https://lnkd.in/gYA8eUnQ
15. Jesse Anderson - https://lnkd.in/gYaS58E4
16. Andreas Kretz - https://lnkd.in/gWTNDwUg
17. DataCamp - https://lnkd.in/gEen6W7U
18. Data Engineering Weekly - https://lnkd.in/g7CA7tHM
19. The Analytics Engineering Hub - https://lnkd.in/gyMrwzPM
20. Atlassian Engineering - https://lnkd.in/grvHZskQ

The gap isn't just technical - it's educational, emotional and human.
Let's make AI less scary and more simplified as a support system.

Engineers can build amazing things. 
But we also need to bridge the gap between "𝗵𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀" and "𝘄𝗵𝘆 𝘆𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝗰𝗮𝗿𝗲."

So, where are you right now — building it, learning it, or still figuring out what all the hype is about? 💬

DevOps & CloudOps | AWS | Azure | GCP | Terraform | Jenkins | Kubernetes | Docker | Git | Ansible | CCNA | Linux

Software Engineer skilled in cloud computing like Amazon Web Services (AWS) and languages like Python and databases like SQL and NoSQL,

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