Data Advisor

Director, Data Engineering, Cross-Meta Integrity

Being the first Data Engineering Manager at Facebook London, I have taken a broader responsibility for building and growing the London site, including:
- Grown the London site from 4 Data Engineers to more than 200
- Coached, mentored, and grown people at all levels, from early-career data engineers to executives
- Led recruiting efforts, at all levels, to support the growth of the site
- Led employment-branding efforts, meetups, events, and been keynote speaker at large data events, to increase visibility of our work
- Built communities of Data Engineers, DE Managers, and DE Leaders across the site, fostering collaboration and promoting knowledge sharing across different orgs
- Led performance management processes across the site

Data Engineering Site Lead, London

Protected the community of our users by reducing harmful experiences across Facebook/Instagram/Messenger.
- Led a team of Data Engineers working responsible for the measurement, detection, and enforcement on Integrity problems across the Meta family of apps
- Been responsible for data solutions at very large scale, and very high complexity
- Established the Data Architecture Review Team to ensure consistency and good architecture across all of our data solutions

Data Engineering Manager, Central Integrity, London

Data Engineering Manager for a portfolio of Ads products:
- Built and owned the data infrastructure for a portfolio of products generating more than $10BN/year run-rate
- Worked with the Ads Executive team on a new reporting cadence, to ensure high rigour with regards to our top-line metrics
- Worked closely with the product teams on their roadmaps, direction, and strategy
- Been part of the site leadership group, coaching, supporting, and developing our managers community
- Led collaboration with Data Infrastructure teams, to significantly improve our tooling, especially around dashboarding, data visualisation, root-cause analysis, and data discovery

Data Engineering Manager, Ads and Business Platform

WorldRemit

Established the Big Data team in WorldRemit.   Responsible for transforming WorldRemit to a data-driven company:
- Defined the Big Data strategy and practice for WorldRemit
- Hired, trained and managed the Big Data team
- Built the foundations of the Big Data Platform, while solving real business challenges along the way
- Reconciled, cleansed and decommissioned redundant data sources to increase clarity
- Built data applications to improve customer satisfaction, pricing strategy, and business performance
- Developed the analytics muscle in the different departments (product, compliance, marketing, etc.)
- Managed the company Data Warehouse and reporting tools

Head of Big Data

PayPal

Established a team of Data Engineers within PayPal Risk.
The team was a part of the Global Risk Science group, which is primarily responsible for developing large-scale data products and providing important insights on our customers.

Key responsibilities:
-	Managing the relationship and interface with the various data and infrastructure groups in PayPal
-	Supporting the development life-cycles of Risk analytical components and models
-	Operationally managing offline batch jobs at staging, and facilitating migrations of jobs to PayPal's production environments
-	Training Risk analysts and data scientists on all aspects of data usage

Data Management Manager

Pontis

Established a team of Business Analysts, in a small fast-growing startup company working in the Telco industry, responsible for all analytical tasks and services given to the Marketing Consultants.

Was responsible for:
-	Recruitment and onboarding all team members
-	Developing analytical methodologies, procedures, working-processes and tools
-	Consistently providing fast, high-quality solutions and insights to the Marketing Consultants
-	Turn raw data into meaningful business insights that drive our customers' marketing strategy

Head of Data Science and Analytics

John Bryce

Managed the Database Training Department in John Bryce, largest technology education company in Israel.

Key responsibilities:
-	Developing Database Training courses, onboarding the instructors and providing advanced training
-	Supporting Sales, Marketing and Customer Contact Center with all database related inquiries
-	Managing the professional aspects of Israel’s largest computing convention (Oracle Week)
-	Initiating partnerships and developing businesses with potential partners and stakeholders

Head of Database Training Department

Ministry of Defense

DBA Team Lead

Database Administrator

SHIP Computing

Computer Technician

I am an advisor on a mission to help companies and people find a path to growth with data.

Here are 3 primary ways I can help you:
1. [Companies] Does your company struggle with data? Do you spend a lot and don’t get enough value in return? Is there lack of trust between stakeholders and data teams? Do you see data as a massive opportunity for your company? Would you like to start building your data team and data function in your organisation, and not sure how to go about it?

→ If the answer to any of these questions is “yes” – I can help! I help companies succeed with their data, and I can help you too with your data journey. Let’s talk.

2. [Data Startups] Building and innovating in the data space? 

→ As an advisor, I help data startups succeed by helping them understand the market, their customers, and their needs. I harness my domain knowledge, my network, and my experience, to help you succeed.

3. [Individuals] Are you an individual data practitioner or a data leader, who is ready to take their career to the next level? Are you ready to get out of your comfort zone to 10x your growth?

→ Over the past 20 years I have grown and mentored individuals, all the ways from interns to senior executives. My approaches and playbooks are proven by 100s of individuals. Join the movement and learn how you too can achieve your goals. Not sure what your goals are? don't worry, we will figure it out.

About me
----------
A data executive with 20+ years of experience in every aspect of data across the organisation. I have proven expertise in driving business and individual growth. My experience ranges from startups like Pontis and WorldRemit to big tech giants such as Meta and PayPal. 

At Meta I served as the Data Engineering site lead in London, growing the function from 4 Data Engineers to more than 200. I have managed the data for a $7BN Ads business, and in my last role, I was the Director of all Trust and Safety Problems on Facebook, Instagram, and Messager.

Since leaving Meta, I started my successful hyper-growing business.

People in data: you don't need to learn every tool out there, that's useless. Just focus on the basics.

The basics:
SQL, Python, Jupyter Notebook, dbt, Fivetran, Snowflake, Tableau, Apache Kafka, Airflow, Iceberg, Looker, Databricks, Upriver, Vertex AI, Metabase, TensorFlow, Plotly, H2O, BigQuery, Domo, Pulsar, Power BI, Airbyte, Neo4j, Redash, Dataform, AutoML, Oracle, GraphQL, MLflow, Redshift, Google Data Studio, Meltano, Grafana, Presto, Talend, SQL Server, Superset, Jenkins, Stitch, Azure Event Hubs, Trino, ThoughtSpot, Kinesis, Seaborn, PostgreSQL, RabbitMQ, Spark, Weights & Biases, Git, Statsig, scikit-learn, Rivery, MariaDB, Elasticsearch, Informatica, QlikView, Apache Superset, Pub/Sub, MySQL, Qlik Sense, PyTorch, Kibana, DuckDB, Dagster, Kubeflow Pipelines, Flink, ClickHouse, Athena, Beam, SageMaker, Mode

Shachar Meir

Data executive with 20+ years of experience in every aspect of data across the organization (data engineering, data science and analytics, reporting, business-intelligence, education, and data management). Technically oriented and and all about getting things done.

Excited about transforming organisations and products, helping them make the most out of their data, and drive with data.

I am a team player with can-do attitude, great social skills, and a lot of enthusiasm for technology and difficult problems.
My focus is on good execution, while having lots of fun along the way!

When I'm not working, I enjoy flying airplanes and helicopters, cooking, and having quality time with my family.

Data Advisor | ex-Meta | ex-PayPal | Speaker | Airplane and Helicopter pilot 🚁

Meta had the best Data Engineering interviews I’ve seen..

.. but not for the reasons you might think.

By the time I joined Meta in 2017, I had hands-on experience with many data technologies.

None of these technologies applied to Meta.
The company had its own proprietary stack:
- dashboarding tool
- orchestration tools
- experimentation tools
- storage and compute engines

Everything was built in-house.

We couldn’t interview for the Meta tech stack, so we focussed on two things:
- personality
- understanding of CONCEPTS 

It didn’t matter if your background was SQLServer, Oracle, Redshift, or Hadoop – the fundamentals were the same:
- data architecture
- troubleshooting
- data modelling
- product sense
- dashboarding

Many companies today are obsessed with hiring for specific tools and technologies.

They are missing out on lots of great people.

♻️ Repost if you think that concepts are more important than tools

Building 700 dashboards is EASY.
Building 6 dashboards is HARD.

This is a very common scenario I see:
- medium-sized company 
- been in existence for a several years
- analytics team is a dashboards factory, every task results in a new dashboard
- whenever an employee leaves, the same dashboard can be created again because nobody knows it exists

Voila! That’s how you get to 700 dashboards.

Then I tell them: “a company of your size shouldn’t need more than 6-7 dashboards”.

They are visibly confused.
It makes a lot of sense to them.
They don’t even know where to start.

Building 700 dashboards is EASY.
You don’t need to know what you’re doing.
You just need to be consistent.

Building 6 dashboards is HARD.
You need to do a lot of research, planning, thinking, and design.
You need to know what you’re doing.

♻️ Repost if you’ve experienced the dashboards hell

People think that job security is about not getting FIRED.

They are wrong.

Anyone who is full-time employed can get fired.
That’s how companies work.

Companies hire and fire by the need of the hour. It’s by design.

When there’s a lot of money – accelerate growth and hire more people.
Less money? Play more conservative, cut costs, and create a longer runway.

It means that good people can get fired, even if they do everything right, are loyal, and perform well.

For me, job security is keeping yourself in a position you can easily find another job:
- stay current on tools and technologies
- build and maintain your network
- work on your brand
- have options

Don’t worry too much about your current job.
It’s the next one that matters.

♻️ Help your network by reposting this

Saurav Mukherjee

me: “do you have a data strategy?”
VP Data: “of course, we use BigQuery, Rivery, and dbt”

This is something I see too often.
Way too often.

Data teams who think strategy is a list of tools.

It’s not.

A good data strategy should at least include:
1. Business Purpose - how are you going to use data to create value for the business?

2. Data Governance and Quality - how do you protect the data and its integrity over time

3. Architecture and Infrastructure - how does your data platform work and integrates with other systems

4. People and Processes - team makeup, operating model, etc.

5. Success KPIs - what do you measure to ensure your data is driving value and is in a good state

What else would you include?

♻️ Repost to help data leaders in your network

I talk to many people who are UNHAPPY with their life.

They have a job they don't like.
But it pays the bills, so that's good, right?
And the job market isn't great at the moment..

That's all true.

But if you're in this situation, there's one question you need to ask yourself:
"How would I feel if I were in the same place 3 years from now?"

If only the thought of that makes you horrified, it means that you're stuck.
Take action TODAY to change your course.
Live life on your own terms.

You won't get this time back.

This mindset changed everything for me.

Early in my career, I just worked on my tasks and tried to do a good job.
Then I realised it wasn't good enough:
- I built things only to find out that they weren't what was really needed
- I worked on things that haven't been used
- I had no visibility into what's coming next
- Priorities changed on me all the time

That's when I started adopting what my VP at Meta used to call a "data leadership" mindset.

It completely transformed my career and changed the way I do things.

I talked about it in my "Why Data Professionals MUST Lead (or Fail)" video.
Link in the comments.

This chart is all over Reddit and X.

There's one big thing missing here..

Zach Wilson also posted about it earlier this week. 
He made great points which I completely agree with – this is more about macro-economics rather than AI.

If you haven't read it, I'll link it in the comments.

But there's one more thing missing in this chart – FREELANCERS!

The number of people who reached out to me in the past 6 months alone, telling me they are quitting their job to become freelancers and asking for help, is mind blowing!

There are big freelancing communities such as Jonathan Stark's Ditcherville and Benjamin Rogojan's Technical Freelancer Academy, and they're exploding.

This is no longer a trend.
It's a change in the way we work.

------

By popular demand, I've created a list of the best freelancing resources I've come across since I started my journey.
These are things I wish I had on day-1..

DM me if you want it and I'll happily share it (it's FREE!).

Companies – losing top talent is more COSTLY than retaining them.

Earlier in my career I lost the best analyst I had on my team.
They were right to leave, they had a better offer..

The better offer? 15% salary increase.

The tragedy was that they had been in their role for a while and totally deserved a salary increase anyway, but the company wasn't willing to pay.

That’s when I learnt the true cost of losing a top performer:
1. Knowledge and skills lost
2. Impact on the team until a new person is hired
3. The time it takes to onboard the new team member
4. Opportunity cost during the time your position isn't staffed
5. The time spent hiring a new person (and the risk of hiring the wrong one)
6. ...

Add all these factors up, and the cost would be way more than the 15% increase they could have paid.

Invest in your top performers.
It will always pay off in the long-run.

Many people will NEVER achieve their goals.

And not for a good reason..

Too many people are victims of their own personality.

On the outside:
- they say they want to be successful
- they appear to be hard working
- they aim high

But on the inside:
- they blame everybody else for their failures
- they are unwilling to take feedback
- they are too proud to ask for help
- they won’t commit and show up
- they shy away from hard work

And that’s ok, no judgement here.
Let’s just be real..

There are many hurdles between you and your goals.
Don’t make yourself one of them.

Amal' Imangulov absolutely smashed his Data Engineering interviews at Meta!

Want to know how?

Several months ago Amal reached out.
He was interviewing with several companies and asked for advice.

I told him: "let's meet, but have it recorded so others can learn from it too..".

It was a one-hour session, packed with loads of tips and advice for anyone interviewing for Data Engineering roles.

A few weeks later I got a message saying that he got accepted to Meta.
I couldn't be more excited for him!

Link to the video in the comments.
Wishing you all the best Amal 🙌

How to go solopreneur after 20 years of full-time employment.

(and what to expect)

I had the pleasure of joining Jonathan Stark in his podcast.

Jonathan is the GOAT (🐐) of solopreneurship. We talked about:
- My career journey, and going solo after 20 years of employment
- The transition – what went well, what were the challenges
- The first steps I took to get clients
- The importance of networking
- Leveraging social media
- How to create content

and more..

It's been a great conversation packed with so much PRACTICAL advice!

Check it out if you're already freelancing, or if you consider taking this path.

Thanks for having me Jonathan! Pleasure as always.
Link in the comments 👇

♻️ Repost to help solopreneurs in your network

Here’s a list of the most influential creators on LinkedIn in 2025:

(stop wasting your time and follow them!)

- ChatGPT
- Claude
- Gemini

People – thought leadership isn’t about producing masses of generic content.

It’s about:
- thinking deeply
- forming opinions
- articulating them in words

Thanks for your attention to this matter.

The journey with and without mentors.

(the most EFFICIENT way to boost your career)

This came up in several conversations I had this week.
People asked about the importance of mentors in your career.

Here's how I see it:
- assuming you have goals (this is a big assumption, I know..)
- many people would be able to achieve their goal eventually
- the path can be very convoluted though, going around in circles, back and forth, shooting in all directions, and eventually hitting the goal
- the right mentors have done that in the past, they know what it takes to achieve the goals, and keep you accountable and on track

Let's say that your goal is to get to VP level – what's the difference between getting there in 8 years vs. 14 years?
Multiply the numbers and see that it's actually A LOT.

Mentors are the best way I know to expedite your growth, and overcome challenges.

It's not cheating – it's just asking experienced people for help..

♻️ Repost to help others in you network

There’s a common belief that most data leaders fail in their first 12 months.
(that’s wrong)

Some of them last as long as 18 months..

Product Manager: “we’ve decided to build this product. Give me data that shows it’s the right decision”

Me: “mate, there’s not enough data in the world to suggest this is the right decision..” 🤦🏻‍♂️

People underestimate the importance of experts.

We don’t need to reinvent the wheel *every* time.

me:"when was the last time you met a Director+ in your company?"
them: "last week. I used the techniques you shared in your course"

This came up in a conversation today.
I was so humbled by that!

Thousands of data professionals took my Career Unblocking Mini-Course, since it was launched. 

I've received feedback from people saying that:
- it helped them get the promotion they wanted
- it helped them build relationships with leadership
- the frameworks I shared made leaving their comfort zone easier
- it pushed them to understand their business and grow their impact

MASSIVE success.

I you haven't taken it yet, and you're ready to push your career to the next level, you can still sign up.
Link in the comments.

I am planning to keep it FREE until the end of the year.
However, I am going to announce big changes very soon.

Stay tuned.

♻️ Repost to help other data professionals in your network

How I would start a Data Freelancing business in 2026:

Whether you're already freelancing, or you're considering freelancing as a career path – you should watch this conversation with Dheeraj Mishra.

We talked about:
1. Positioning and finding your niche
2. Ways to find clients: networking, marketing, sales, partnerships, organic
3. Identifying business problems
4. How to showcase your work
5. Testimonials and reviews
6. How to talk to strangers

And the best part – we tried to keep it SIMPLE and PRACTICAL.
The full conversation is on my channel (link 👇).

♻️ Repost to help others in your network

Technical people use the WRONG metrics to measure success.

That's why they keep failing.

Think about the difference between technical people and the rest of the business.

Technical people often measure success in the following ways:
- Pace of delivery
- Quality of delivery
- Quality of operations

Other people measure:
- Usage and adoption
- Revenue generated
- Value creation

I'm simplifying a bit here, but my point is that many tech people measure OUTPUT, while the rest of the business measures OUTCOME.

No value has been created if you've:
- Built the best churn prediction model and it's not being used
- Built the best dashboard and no decisions have been made as a result
- Built the best data model for your product, and people use raw data instead

OUTPUT ≠ OUTCOME

Want to break the ceiling?
Forget about output and start measuring outcome.

In fact, most people I see can do much less and achieve greater impact..

♻️ Repost to help your network

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