Cassie Kozyrkov

Kozyr

Speaking and advisory services. Request form: makecassietalk.com

Kozyr was renamed from Data Scientific.

Chief Executive Officer

Federal Reserve Bank of New York

Served a full two year term as Innovation Advisor and Councillor at the Federal Reserve Bank of New York, with a focus on the impact of AI on the financial markets and global policy.

Innovation Advisory Council Member

Google

Decision Intelligence is an interdisciplinary field concerned with all aspects of decision-making.  It combines Data Science (statistics, machine learning, AI, analytics) with the Behavioral Sciences (psychology, neuroscience, economics, and the managerial sciences).

I serve as a thought leader in this new discipline, guiding Google and the world in making safe, effective use of AI and data.

Chief Decision Scientist

My mission is to democratize artificial intelligence and guide business leaders in harnessing the transformative power of machine learning.

Chief Data Scientist, Office of the CTO

Youngest ever member of Google's Office of the CTO, specializing in AI and data science.

I'm also a coauthor of Google's Technical Director training program to teach others how to succeed in the role.

Technical Director, AI and Data

My roles are: statistician, machine learning expert, mentor, data scientist, evangelist, decision scientist, and consultant.  I bridge the gap between leaders and quantitative analysts / researchers.

As Google's statistics and machine learning sensei:
* I've advised 200+ teams at Google on their analytics processes.
* My ideas have sparked 400+ new projects.
* I've trained 20,000+ analysts/leaders/engineers in rigorous data-driven decision-making and/or using machine learning to transform their work.

Largest projects in: Search Advertising, Google Maps, Machine Intelligence, Infrastructure, YouTube

Statistician

Self-Employed

Data science consulting and analytics services for clients from government organizations, private sector firms, and academia.

Data Science Consultant

North Carolina State University

Statistics Lecturer

Statistical Computing, Advanced Topics in Statistics: Big Data, Special Topics in Statistics: Data Mining, Applied Time Series Analysis, Applied Multivariate Data Analysis, Advanced Probability and Measure Theory, SAMSI Computational Advertising, Statistical Methods II, Statistical Consulting, Statistical Theory I, Statistical Theory II, Linear Models and Variance Components, Stochastic Processes, Doctoral Research, Supervised Doctoral Teaching, R Programming

Mathematical Statistics PhD Student

Duke University

Psychology & neuroscience with specialization in transcranial magnetic stimulation (TMS) methods and the science of decision-making.

Neuroeconomics PhD Candidate

Center for Cognitive and Social Neuroscience at the University of Chicago

Coordinated lab research projects and team effort. Designed and ran experiments.

Project Manager

Charitable contributions and decision-making research with Professors Steve Levitt and John List.

Research Assistant - Prof. John List

Cassie is Google’s former Chief Decision Scientist and a sought-after AI advisor, working with senior leaders at places like Lenovo, NASA, and Gucci. She is currently CEO of Data Scientific and is known for founding the field of Decision Intelligence.

Cassie is a beloved personality in the data and AI community, followed by over half a million tech professionals. She is also a renowned keynote speaker and writer. ( If you'd like her to speak at your event, here's her booking form: makecassietalk.com )

In almost 10 years at Google, Cassie personally trained over 20,000 Googlers in data-driven decision-making and AI and has helped over 500 projects implement decision intelligence best practices. Before that, she served in Google's Office of the CTO as Chief Data Scientist. The rest of her 20 years of experience was split between data science, consulting, lecturing, and academia.

If you've had a good chuckle while learning about AI, statistics, or decision-making, chances are you've encountered her writing, which has reached millions of readers. She has been honored as a LinkedIn Top Voice for 5 years and as a #1 Artificial Intelligence writer on Medium for 7 years.

Speaking and advisory services. Request form: makecassietalk.com

86% of enterprises plan to invest more in agentic AI.
6% trust agents to run core processes.

Let's talk about that gap!

Harvard Business Review just released some findings about tech decision-makers (n=603):
🤖 86% expect agentic AI investment to grow in 2 years
🤖 6% fully trust agents with end-to-end processes
🤖 ~25% say they're "ready" on infrastructure, data, security, and governance

That lack of trust is a good thing, in my opinion. Trusting complex probabilitistic systems without re-archtiecting complex processes is a recipe for kablooies. If anything it should give you hope. The trouble is that the redesign is difficult except in narrow use cases.

The model isn't the bottleneck anymore.
The plumbing and the people are.

Fragmented apps and data → agents can't get reliable context 
Legacy processes → never designed for autonomous execution 
Security and compliance → no playbook for "agents at the core" 
Employees → unclear if agents are tools or replacements

The report points to enterprise orchestration as the missing layer—connecting systems, standardizing actions, enforcing governance so agents behave predictably instead of becoming shadow IT.

Old question: "Which LLM should we use?"
New questions:
Which processes are we willing to redesign around agents?
How do we orchestrate data, tools, and guardrails so we can trust them with real work?

In 3 years, the gap won't be "who has agents."
It'll be who rebuilt their processes so agents can actually run them.

Where is your team right now?
A) Still experimenting at the edges (help desks, copilots) 
B) Re-architecting workflows so agents own real outcomes 
C) Somewhere in between, trying to figure out where to start
D) Waiting and watching others stub their toes

Drop your letter below. Your comments make my (digital) world 'round.

P.S. On a completely different note,  my Decision-Making with ChatGPT course is running on demand right now with a live Q&A with me next week. Come ask me anything! decisiongptcourse.com

CEO, Google's first Chief Decision Scientist, AI Adviser, Decision Strategist, Keynote Speaker (makecassietalk.com), LinkedIn Top Voice

Agentic HBR

Been wondering when my next cohort will be? Surprise! The time is NOW. If you missed the previous run or have been waiting to join, enrollment is now live:

 👉 https://lnkd.in/eG7EBtTv

The last cohorts made something clear: leaders aren’t looking for more information. They’re looking for clearer judgment. ChatGPT helps - but only when you use it in the right parts of the thinking process.

In this cohort, we’ll continue the work of turning AI into a genuine thinking partner instead of a shortcut. You’ll learn how to:

• Use ChatGPT to reduce effort without losing rigor
• Avoid the trap that turns “AI-assisted” into lazy thinking
 • Spot cognitive biases before they distort outcomes
 • Separate luck from decision quality
 • Build a decision scribe so you finally learn from your choices
 • Operate with confident judgment instead of drowning in advice

You’ll get 30+ on-demand videos (available as soon as you enroll), a 2-hour live Q&A with me (at 1 PM ET Dec 18), prerecorded Q&A from earlier cohorts, templates, frameworks, and a global community.

If you're working on sharpening your thinking in an AI world, Cohort 3 is now open.
 👉 https://lnkd.in/e-RaxVJJ

If this resonates, a repost or subscribing to decision.substack.com helps the course reach the leaders who need it.

#DecisionIntelligence #AILeadership #StrategicThinking #DecisionMaking #AI

Wavicle Data Solutions

Lead Data Engineer

LinkedIn

LinkedIn Learning Instructor

Globant

Senior Data Engineer

CRISIL Limited

Senior Analyst - Data Engineer

Quantiphi

Data Engineer

Tata Consultancy Services

System Engineer

Assistant System Engineer

Assistant System Engineer - Trainee

InsideSherpa

KPMG Data Analytics Consulting Virtual Internship

Monisha Enterprises

•	Data mining and Extraction Transformation Loading using R programming.
•	Data cleaning and processing for Data analysis done using Python using NumPy, pandas, SciPy, Scikit. Data modelling and prediction was done using algorithms – Logistic Regression, Time Series Forecasting, Clustering and Classification.
•	Data Visualization done using Tableau for generating reports to enhance the productivity of the products.
•	Built a Data Warehouse on MSSQL Server for ERP System of the enterprise to organize the data for future processing.

Data Analyst Intern

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 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

When everyone talks about data… but only one person actually fixes it. 

👉 It’s always the Data Engineer who climbs into the pit and makes the system work.

If you’re starting your journey as that person — the quiet builder behind every AI success — here’s a structured reading list and GitHub resources to build rock-solid foundations.

1. 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀
 • Tools: Excel, Power BI, SQL, Python
 • Focus: Automate reports, clean data, build dashboards
 • AI Boost: Use Copilot or ChatGPT to write Python scripts, generate SQL queries, and debug faster

2. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺
 • Tools: Pandas, dbt, Git, Jupyter
 • Focus: Write modular code, version control, transform data
 • AI Boost: Let AI help refactor messy code, explain Git workflows, and generate dbt models

3. 𝗗𝗮𝘁𝗮 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 
 • Tools: Airflow, Prefect, dbt, Databricks
 • Focus: Build ETL pipelines, schedule jobs, test transformations
 • AI Boost: Use AI to design pipeline architecture, write DAGs, and troubleshoot errors

4. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗖𝗼𝗿𝗲
 • Tools: AWS/GCP/Azure, Spark, Kafka
 • Focus: Scale workflows, handle big data, deploy in cloud 
 • AI Boost: Use AI to generate infrastructure-as-code, optimize Spark jobs, and simulate Kafka streams

5. 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 & 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀
 • Tools: GitHub, Streamlit, FastAPI, LinkedIn
 • Focus: Build end-to-end projects, document, share, and network
 • AI Boost: Generate project ideas, write documentation, and create interactive dashboards

But Will AI Replace Data Engineers❓
It’s a common fear. But here’s the truth:
AI won't take your job; 
it will just automate the boring parts, raising the floor for everyone else.

Explore these data engineering projects to upskill and level up-

Beginner:
ETL pipeline using Python and SQL by Ankit Bansal:
- https://lnkd.in/gTdCV9aJ
- https://lnkd.in/gxEYM3Bb

Intermediate:
- Data warehouse solution using Snowflake, dbt by Shashank Mishra 🇮🇳 : https://lnkd.in/gf-c5TR7
- Mr. K Talks Tech : https://lnkd.in/gt9JAkRt
- Snowflake project by Data Engineering Simplified : https://lnkd.in/gXRWyHpc
- Apache Spark project by Ankur Ranjan: https://lnkd.in/gt9JAkRt

Advanced: Implement a real-time streaming data processing
- Darshil Parmar : https://lnkd.in/ghiEsa7P
- Yusuf Ganiyu : https://lnkd.in/giYwJaCS

Cloud Projects:
- Microsoft Azure by Data Engineering Simplified - https://lnkd.in/gx3aqzKU
- Microsoft Azure by Sumit Mittal - https://lnkd.in/dX2yma5b
- Amazon Web Services (AWS) by Darshil Parmar - https://lnkd.in/gJg7KV-7
- Google Cloud by Anjan - https://lnkd.in/gjHbmCaM

System Design concepts with:
- Alex Xu - https://lnkd.in/gGdgJRDd
- Design Gurus - https://lnkd.in/gaphzp89

DataExpert.io handbook compiled by Zach Wilson -https://lnkd.in/gb4xBQJy
🔥 𝗔𝗜 𝘄𝗼𝗻'𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀—𝗶𝘁 𝘄𝗶𝗹𝗹 𝗺𝗮𝗸𝗲 𝘁𝗵𝗲 𝗴𝗼𝗼𝗱 𝗼𝗻𝗲𝘀 𝘂𝗻𝘀𝘁𝗼𝗽𝗽𝗮𝗯𝗹𝗲 𝗮𝗻𝗱 𝗹𝗲𝗮𝘃𝗲 𝘁𝗵𝗲 𝗿𝗲𝘀𝘁 𝗯𝗲𝗵𝗶𝗻𝗱.

From 'What does that even mean?' to 'I got this' in 30 terms!

Data Engineering isn't about tools; it's about understanding the language. 

Master these concepts, and you’ll see the entire system relatable, not just the pipeline parts.

𝗗𝗔𝗧𝗔 𝗠𝗢𝗩𝗘𝗠𝗘𝗡𝗧
→ ETL vs ELT: Cook before serving vs serve then cook. ETL transforms first, ELT loads raw and transforms later.
→ Stream vs Batch: Live TV (process as it arrives) vs binge-watching (scheduled chunks).
→ Data Pipeline: Automated highway moving data from source to destination.

𝗗𝗔𝗧𝗔 𝗦𝗧𝗢𝗥𝗔𝗚𝗘
→ Data Warehouse: Organized filing cabinet—structured, optimized for analytics (Snowflake, BigQuery).
→ Data Lake: Your attic—stores raw everything: CSVs, logs, videos. Cheap but messy.
→ Data Lakehouse: Lake affordability + warehouse reliability (Delta Lake, Iceberg).

𝗗𝗔𝗧𝗔 𝗦𝗧𝗥𝗨𝗖𝗧𝗨𝗥𝗘
→ Star Schema: Central fact table + dimension tables. Simple, fast.
→ Snowflake Schema: Normalized star schema—saves storage, complicates joins.
→ Data Modeling: Designing table relationships so queries don't become nightmares.

𝗗𝗔𝗧𝗔 𝗣𝗥𝗢𝗖𝗘𝗦𝗦𝗜𝗡𝗚
→ OLTP: Real-time transactions (your bank app).
→ OLAP: Analytics on millions of rows (your dashboard).
→ Partitioning: Slice tables by date/region so queries don't scan everything.
→ Sharding: Split data across servers—multiple checkout lanes.

𝗗𝗔𝗧𝗔 𝗗𝗜𝗦𝗖𝗢𝗩𝗘𝗥𝗬
→ Data Catalog: Library index—find datasets, understand metadata.
→ Data Lineage: Family tree—track where data came from and where it's going.
→ Indexing: Shortcuts so databases don't scan every row.

𝗤𝗨𝗔𝗟𝗜𝗧𝗬 & 𝗚𝗢𝗩𝗘𝗥𝗡𝗔𝗡𝗖𝗘
→ Data Quality: Accurate, complete, consistent—garbage in = garbage out.
→ Data Governance: Access rules, security, compliance.
→ Caching: Store frequent data in fast memory.

𝗦𝗬𝗦𝗧𝗘𝗠 𝗗𝗘𝗦𝗜𝗚𝗡
→ Distributed Systems: Multiple machines working as one.
→ Message Queue: Buffer for async communication (Kafka, RabbitMQ).
→ Orchestration: Workflow conductor (Airflow, Prefect).
→ Fault Tolerance: Keep running when parts fail.
→ Scalability: Grow without rewriting everything.

💡 Reality Check: Tools change yearly. These concepts? Forever.

Whether you're a DE, analyst, or ML engineer—mastering these puts you ahead of 90% of the field.

Image Credits: Shalini Goyal

Mastering these fundamentals is your true skill. 
Tools change, but the need for reliable, scalable, and governed data pipelines stays forever.
#data #engineering

🏗️ Confused by Data Lake vs Warehouse vs Lakehouse vs Mesh?

Selecting the right architecture is game-changing for scalable, trustworthy analytics.

1️⃣ Data Lake
Think of a natural lake — it holds everything: clean water, mud, fish.
Similarly, a Data Lake stores raw data in any format. Flexible, but you’ll need to clean it before use.

2️⃣ Data Warehouse
Imagine a warehouse — everything is neatly packed and labeled.
A Data Warehouse stores structured, cleaned data ready for dashboards and reports. Reliable, but less flexible.

3️⃣ Data Lakehouse
Picture a house by the lake — you get the beauty of the lake and the comfort of a home.
A Data Lakehouse combines raw + structured data. Best of both worlds!

4️⃣ Data Mesh
Think of a network of homes — each family manages its own space.
Data Mesh decentralizes data ownership. Teams treat data as a product and manage it themselves.

🎯 THE SIMPLE TRUTH:
→ Starting out? Use a Data Warehouse (BigQuery, Snowflake, Redshift)
→ Need cheap storage for logs/raw data? Add a Data Lake
→ Want both? Modern Lakehouses (Databricks, Iceberg) are solid
→ Data Mesh? Don't even think about it until you're at massive scale

🎓 Major Takeaways from my experience:
→ Architectures must be aligned with specific project needs for optimal results.
→ Hybrid models like Lakehouse unlock flexibility and performance.
→ Evolving architectures foster innovation and reduce bottlenecks in data pipelines.

Embedding flexible, forward-thinking architecture is vital for data-driven success. 

Companies leading these transformations are able to pace up by 3-5x reducing infrastructure costs by 40-60% through right-sized, purpose-built data systems delivering insights.

Here are some resources to understand more about each of them -
→Designing Data-Intensive Applications by Martin Kleppmann - https://lnkd.in/drm6wQvH
→The Data Warehouse Toolkit by Ralph Kimball - https://lnkd.in/daWQwZ7Q
→Databricks Lakehouse Fundamentals (Free Course) - https://lnkd.in/gQEA-guy
→Google’s Data Mesh Playbook - https://lnkd.in/gumDAdfk

𝘈𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘪𝘴 𝘵𝘩𝘦 𝘣𝘢𝘤𝘬𝘣𝘰𝘯𝘦 𝘰𝘧 𝘪𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘰𝘯 𝘪𝘯 𝘥𝘢𝘵𝘢 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨.

73% of data pipeline failures happen because of dependency issues, not bad code.
Cron jobs don't care if yesterday's data never arrived. They'll happily run on empty tables and produce garbage.

Airflow? It checks, retries, alerts, and refuses to cascade your failure downstream.

→ Beyond scheduling Airflow gives an important feature of 𝗗𝗔𝗚𝘀(𝗗𝗶𝗿𝗲𝗰𝘁 𝗔𝗰𝘆𝗰𝗹𝗶𝗰 𝗚𝗿𝗮𝗽𝗵𝘀), that helps to monitor, retry and gives flexibility to organize pipelines with minimum/less code.

🤔 Data Engineers often misinterpret Airflow just as an orchestration tool.

Apache Airflow is an open-source platform designed to programmatically author, schedule, retry and monitor workflows. 

🎼Think of it as the "conductor of an orchestra", where each musician (your data tasks, like data extraction, transformation, loading, or model training) knows when to play, what to play, and what happens if they make a mistake.

Most data engineers automate the wrong things at the wrong time.

Here's the framework I use after 8 years of building production systems:
✅ AUTOMATE WHEN: 
→ Task runs daily/weekly 
→ Human errors cause outages 
→ Work blocks other priorities
→ Team growth = more manual work
Examples: Reports, schema checks, alerts

❌ DON'T AUTOMATE WHEN: 
→ Task happens quarterly 
→ Requirements change weekly 
→ Process isn't understood yet 
→ Manual steps reveal insights

My rule: If it’s done 3+ times, script it; 10+ times, automate it; fails 5+ times, redesign it.
Automate what matters, when it matters—not everything!

Airflow isn’t just about running tasks.

🕑 It’s about building trustworthy, scalable, and observable pipelines that run reliably at 2am — even when you’re not watching.

Airflow gives structure to your chaos.
DAGs give clarity to your pipelines. 
That’s why DEs love them ❤️

Real Example: 
Instead of: 
❌ Cron jobs that fail silently 
❌ Manual dependency management
❌ No visibility into failures
You get: 
✅ Visual workflow monitoring 
✅ Automatic failure notifications
✅ Smart task scheduling 
✅ Easy debugging & restarting

Here's an amazing Airflow cheatsheet with some fundamentals concepts and interview questions to prepare for data engineers. 👇

Carousel Credits -  Futurense.com

Willing to learn more, here's the link to Astronomer free learning path - https://lnkd.in/g8-EWBda

Also, level up with Marc Lamberti's Udemy course on Airflow 3 - https://lnkd.in/gwArwaME

Airflow

Success and failure are separated by one decision: 
The choice to keep moving when everything says stop.

Agree?

Data governance is not the security guard saying “no”.

It is more like traffic lights in a busy city – a few rules that let everyone move faster and safely at scale.

Behind every confident decision is invisible governance doing the heavy lifting.
You know that feeling when you pull a report and immediately trust the numbers? 

When you can find the exact data you need in seconds? 
When your dashboard doesn't contradict itself?
That's not luck. That's governance.

🚫 The Misconception vs. ✅ The Reality

What People Think Data Governance Is:
 ❌ The "Department of No" that blocks data access
 ❌ Bureaucratic red tape that kills innovation
 ❌ A 500-page policy document gathering digital dust
 ❌ IT's way of controlling everything

What Data Governance Actually Is:
 ✅ The GPS system for your organization's data
 ✅ The quality control that makes "yes" possible
 ✅ The shared language that stops data arguments
 ✅ The trust layer that turns data into decisions

As a data engineer, you're not building barriers. You're building trust. 
You're not slowing things down. You're preventing the slowdowns that come from:
→ Wrong data in production
→ Compliance violations
→ Metric arguments
→ Security incidents
→ Data disasters

𝘎𝘰𝘰𝘥 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘪𝘴 𝘪𝘯𝘷𝘪𝘴𝘪𝘣𝘭𝘦. 𝘉𝘢𝘥 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘪𝘴 𝘪𝘮𝘱𝘰𝘴𝘴𝘪𝘣𝘭𝘦 𝘵𝘰 𝘪𝘨𝘯𝘰𝘳𝘦.

👉 Here's a quick Reality Check -
💵 Bad governance costs you:
→ 3 AM panic calls
→ Executive trust
→ Real money (hello, compliance fines!)
→ Your sanity

✅ Good governance gets you:
→ Predictable, reliable data
→ Business stakeholder love
→ Career advancement
→ Actual weekends

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?

Here are some incredible folks to follow in the field of Data Governance -
Chad Sanderson
George Firican
🎯 Mark Freeman II
Piotr Czarnas
Dylan Anderson

𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀𝗻'𝘁 𝘁𝗵𝗲 𝗼𝗯𝘀𝘁𝗮𝗰𝗹𝗲 𝘁𝗼 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻—𝗶𝘁'𝘀 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘀𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲.

"I understand Git—I know git commit and git push."
That's like saying "I understand flying—I know takeoff and landing."

Everything in between? That's where pipelines break, data gets lost, and 3 AM incidents happen.

That the biggest myth in Data Engineering!
Even after 8+ years in tech, I've seen teams struggle not with writing code, but with managing it.

Git for Data Engineers: Understand Why It's Different
The Data Engineering Reality:

You're not just versioning code. You're versioning:
→ Pipelines that run on schedules
→ SQL transformations that power dashboards
→ DAGs that orchestrate entire workflows
→ dbt models that define business logic
→ Config files that control production behavior

𝘖𝘯𝘦 𝘸𝘳𝘰𝘯𝘨 𝘮𝘦𝘳𝘨𝘦 = 𝘗𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝘥𝘰𝘸𝘯. 𝘋𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥𝘴 𝘣𝘳𝘰𝘬𝘦𝘯. 𝘊𝘌𝘖 𝘢𝘴𝘬𝘪𝘯𝘨 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴.
Git isn't optional. It's your safety net.

🎓 Git Skills Every Data Engineer Needs

Level 1: Survival 
→ Clone, commit, push, pull
→ Create branches
→ Basic merges

Level 2: Proficiency  
→ Pull requests & code review
→ Resolve merge conflicts
→ Rebase vs merge (know the difference)
→ Cherry-pick commits

Level 3: Mastery 
→ Interactive rebase for clean history
→ Git hooks for automation
→ Submodules for shared code
→ Advanced conflict resolution

Here's a some resources to upskill with Git:
• Learn Git Branching - https://lnkd.in/dvPvYEeC
• Pro Git Book - https://lnkd.in/dasXAAYv

Without Git?
𝗬𝗼𝘂'𝗿𝗲 𝗳𝗹𝘆𝗶𝗻𝗴 𝗯𝗹𝗶𝗻𝗱, 𝗼𝗻𝗲 𝗺𝗶𝘀𝘁𝗮𝗸𝗲 𝗮𝘄𝗮𝘆 𝗳𝗿𝗼𝗺 𝗱𝗶𝘀𝗮𝘀𝘁𝗲𝗿.
With Git?
𝗬𝗼𝘂'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗹𝗶𝗸𝗲 𝗮 𝗽𝗿𝗼, 𝘄𝗶𝘁𝗵 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗿𝗼𝗹.

Image Credits: Priyanka Vergadia

What's your biggest Git fear or confusion as a data engineer? Let's solve it together in the comments.

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The Battle

Cassie Kozyrkov

Pooja Jain

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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);}Cassie Kozyrkov's LinkedIn Performance

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Performance ScoreCassie Kozyrkov's LinkedIn Performance