I help people transition into Data Science & Analytics, quickly & easily...

> Data Science Infinity: https://www.data-science-infinity.com/

> 60-minute Data Science Career Training Session: https://training.data-science-infinity.com/register

> AWS For Data Science & Analytics: https://bit.ly/aws-data-science-analytics

> 2 hour Python, Data Science & ML Intro Course >> https://youtu.be/OSbUA5Q9Cec

Feedback from students:

"I landed my role at Amazon totally thanks to DSI" - Andrea (DSI Student)
"The best program I've been a part of, hands down" - Christian (DSI Student)
"I've learned more than on any other course" - Erick (DSI Student)
"One of the best purchases towards learning I've ever made!" - Scott (DSI Student)

About Me: 

15 years in Data Science at companies including Amazon & Sony PlayStation where I developed and prototyped Machine Learning based features for the PlayStation 5 - several of which have been patented by Sony. 

I've interviewed & screened hundreds of Data Science candidates, and through this process have learned exactly what can differentiate a standout & successful candidate from the rest. 

I authored "The Essential A.I. & Data Science Handbook for Recruitment"​ 

Throughout my career, I've had the opportunity to mentor fellow Data Scientists; from their entry into the field, to developing their technical & non-technical skill-sets, as well as providing guidance around preparing for, and being successful with promotions and interviews.​

Please do reach out for support & advice - I'll be happy to help!

Andrew Jones

Aspiring Data Scientists. Yes, you do need to learn some math.

There is no getting around that.

It underpins a lot of what we do in the field.

But, don't be scared off by this.

You DO NOT need to spend a year reading dusty textbooks before you're allowed to progress, build things, or land your first role.

Don't let anyone tell you otherwise.

Start with statistical concepts such as distributions, hypothesis tests, the central limit theorem, confidence intervals, and probability. 

They are the foundations.

From there, start introducing more advanced mathematical concepts, such as Linear Algebra

But, do this as you start applying things like ML algorithms.

It's so much more enjoyable learning while you're testing and modifying things and seeing what changes and why.

You've got this!

💼 Most Data Scientists don’t fail interviews because they lack skills… they fail because they miss this.

After years of interviewing hundreds of Data Science candidates,  I’ve noticed the same patterns over and over again.

Here are the 5 biggest mistakes that stop talented people from landing roles (and how to fix them 👇).

If you’re preparing for your next Data Science or Analytics interview, this one’s for you!

Swipe through and make sure you’re not making these!

🚀 Want to fast-track your career and land your first (or next) data role in 4–6 months?

Join my free Data Science Career Webinar where I’ll show you exactly what skills, projects, and strategy hiring managers are looking for:
 👉 https://lnkd.in/ekp-jmD2

5 most common mistakes

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

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?

No alternative text description for this image

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

taplio

The Battle

Andrew Jones

Pooja Jain

Followers

Posts

Engagements

Likes

Performance ScoreAndrew Jones's LinkedIn Performance

Performance ScorePooja Jain's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Andrew Jones

Pooja Jain

Followers

Posts

Engagements

Likes

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);}Andrew Jones's LinkedIn Performance

Performance ScorePooja Jain's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreAndrew Jones's LinkedIn Performance