How to Get a Better Data Engineering Job After a Lay-Off or Redundancy

Contents

• Understanding Redundancy in Data Engineering

• Step 1: Reset and Refocus

• Step 2: Clarify Your Skills and Preferred Tech Stack

• Step 3: Update Your CV and GitHub Portfolio

• Step 4: Optimise LinkedIn and Showcase Projects

• Step 5: Reach Out to Recruiters and Hiring Managers

• Step 6: Apply Intelligently and Track Progress

• Step 7: Upskill in High-Demand Tools

• Step 8: Explore Contract, Hybrid and Remote Roles

• Step 9: Take Care of Your Finances and Wellbeing

• Bonus: Top UK Employers Hiring Data Engineers in 2025

• Final Thoughts: Redundancy as Redirection

Understanding Redundancy in Data Engineering

Even data teams get restructured. Redundancy is not a reflection of your technical value—it’s often a result of cloud budget shifts, platform migrations, or company-wide changes.

The market still needs skilled engineers who can build, optimise, and scale data pipelines across industries like finance, retail, logistics, healthcare and government.

Step 1: Reset and Refocus

Start by processing what happened:

• Acknowledge your success and experience

• Identify what you liked and disliked about your last role

• Set your sights on roles that align with your strengths, values and career goals

This is a chance to reposition yourself.

Step 2: Clarify Your Skills and Preferred Tech Stack

List out your core competencies:

• Are you strongest in batch ETL, streaming, or warehousing?

• What cloud platforms do you specialise in (AWS, Azure, GCP)?

• Which tools do you know well? (e.g. Spark, Kafka, Airflow, dbt, Snowflake, BigQuery)

This helps you match quickly with relevant roles.

Step 3: Update Your CV and GitHub Portfolio

Your CV should:

• Begin with a clear summary (e.g. “AWS Data Engineer with 5+ years' experience building scalable, secure pipelines”)

• Emphasise results (e.g. “Reduced query time by 60% through warehouse optimisation”)

• Highlight tools, languages and platforms used

• Link to GitHub or project documentation where possible

Make your work easy to assess.

Step 4: Optimise LinkedIn and Showcase Projects

LinkedIn is critical in data hiring.

Profile Tips:

• Headline: “Data Engineer | BigQuery, dbt, Airflow | Open to Work”

• About section: Include experience, strengths, cloud stack, and what kind of role you’re seeking

• Feature personal or team projects, certifications, blog posts

Sample LinkedIn About Section:

Data Engineer | Cloud Pipelines | BigQuery | Open to Work

I’m an experienced data engineer with 5+ years designing robust data pipelines across cloud platforms. Redundancy gave me the chance to refocus, and I’m now seeking a new opportunity to work on meaningful data infrastructure projects that drive insight and scale.

Stack: Airflow, Python, dbt, Snowflake, Terraform, GCP, Git, CI/CD

Let’s connect if you’re hiring for data engineering roles or collaborating on data transformation projects.

Step 5: Reach Out to Recruiters and Hiring Managers

Be proactive in building connections:

Recruiter Message Example:

Subject: Data Engineer | Available Immediately | Cloud & ETL

Hi [Recruiter’s Name],

I’m looking for a new data engineering role following a recent redundancy. My background includes building cloud-native ETL pipelines, stream processing, and cost-optimised data infrastructure.

Please find my CV and GitHub link attached. I’d appreciate hearing about any relevant roles you’re working on.

Best regards,
[Your Name]
[LinkedIn]
[GitHub]
[CV attachment]

Hiring Manager Follow-Up Example:

Subject: Application – Data Engineer Role at [Company Name]

Dear [Hiring Manager],

I recently applied for the Data Engineer role at your company and wanted to share my enthusiasm. I bring hands-on experience with GCP pipelines, warehouse optimisation, and dbt modelling. I’m currently available following a restructure and would welcome the chance to contribute.

Please find my CV attached. I’d be happy to discuss the role further.

Kind regards,
[Your Name]

Step 6: Apply Intelligently and Track Progress

Avoid applying everywhere at once. Instead:

• Focus on 10–15 high-fit roles

• Tailor each CV using keywords from job specs

• Keep a tracker of where you applied, dates, and follow-ups

• Revisit roles weekly to follow up with hiring contacts

Step 7: Upskill in High-Demand Tools

Use this period to boost your stack:

• Earn or update certifications (Google Data Engineer, Azure Data Fundamentals)

• Learn tools like dbt, Great Expectations, Dagster, or Iceberg

• Take short courses on DataCamp, Udemy, Coursera or Pluralsight

• Document new projects in GitHub or write about them on Medium

Step 8: Explore Contract, Hybrid and Remote Roles

Contract work can provide income and exposure while job hunting:

• Look at freelance data gigs via Upwork or Toptal

• Check UK remote-friendly data roles on www.dataengineeringjobs.co.uk

• Explore hybrid positions in London, Manchester, Bristol or Leeds

Step 9: Take Care of Your Finances and Wellbeing

Redundancy is stressful—don’t overlook self-care:

• Apply for redundancy pay, Universal Credit or Jobseeker’s Allowance

• Seek free budgeting advice via Citizens Advice or MoneyHelper

• Structure your day with job search time, skill building, and rest

• Stay connected to peers and meetups to avoid isolation

Bonus: Top UK Employers Hiring Data Engineers in 2025

1. Spotify (London Data Team)

2. Sky

3. NHS England (Data Platforms)

4. Monzo & Starling Bank

5. The Trade Desk

6. ASOS

7. Sainsbury's Tech

8. Zoopla

9. GOV.UK / GDS

10. Booking.com (UK roles)

11. Babylon Health

12. Palantir UK

13. YouGov

14. BT

15. Deliveroo

Explore live data engineering jobs on www.dataengineeringjobs.co.uk

Final Thoughts: Redundancy as Redirection

Being made redundant is difficult, but it can also be the start of a better chapter. Use this time to reset, refocus, and build the career you truly want in data engineering.

Your skills are valuable. Your next role might be your best yet.

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