Master CV Writing: Avoid Pitfalls and Land Your Dream Data Job
Learn how to write a standout CV that avoids common mistakes, highlights your skills, and helps you secure interviews for top data engineering roles.
Greetings, curious reader,
Crafting a standout CV is crucial for data engineers aiming to land competitive roles. It’s not enough to list tools or platforms—your CV needs to tell the story of your impact. This is especially true when AI-driven systems and human hiring managers review applications.
As a Head of Data Engineering, I’ve reviewed countless CVs. Some were outstanding, but most fell into common traps: they were too technical, generic, or failed to communicate the candidate’s value. I've made similar mistakes, losing opportunities because my CV focused on tasks rather than outcomes.
This guide will help you avoid these pitfalls, build a CV that stands out, and position yourself for success. Whether entering the field or looking to grow, these strategies will give you a competitive edge.
Before we start, here's an example version of my CV. This version is typical for data engineers. It's all true but full of jargon and technical details. Check at the end of the article what a revised version of this CV looks like after applying all the tactics.
One more note: I am not a professional CV writer. I don't have any stats to support what I have to say. In this article, I share my observations as a hiring manager.
🛠️ Pitfall #1: Being Too Focused on Specific Tools or Platforms
Why This is a Problem ⚠️
Many data engineers overemphasise specific tools or platforms. For instance, branding yourself as an “AWS Specialist” might seem like a strength, but it can limit your appeal. Employers value adaptability and problem-solving skills over deep expertise in one tool.
I’ve seen CVs where candidates positioned themselves as "Azure Data Engineers" when the role primarily required AWS. While their skills were transferable, their narrow branding made it unclear whether they would be willing to adapt to new environments. This issue often extends to how tools are described: candidates list technologies but fail to explain how they used them to solve problems.
Hiring managers don’t care about the tools you use as much as the results you achieve. Tools change quickly, but problem-solving skills and adaptability are timeless.
How to Fix It 🧰
↔️ Broaden Your Branding: Instead of locking yourself into one ecosystem, use titles like “Cloud Data Engineer” or “Data Engineer with Cloud Expertise.”
🦖 Show Adaptability: Highlight experiences across different platforms, emphasising transferable skills.
⚠️ Focus on Problem-Solving: Explain how you used tools to address challenges and drive results.
Example Rewrite 📝
❌ Before: “Built Azure Data Pipelines.”
✅ After: “Designed and implemented scalable cloud data pipelines using Azure, with additional experience in AWS and GCP for cross-platform solutions.”
This shift emphasises flexibility and results, making you more appealing to hiring managers.
😵💫 Pitfall #2: Listing Too Many Irrelevant Skills
Why This is a Problem ⚠️
An overloaded CV filled with irrelevant skills can confuse hiring managers. While showcasing every tool you’ve encountered is tempting, this approach dilutes your expertise and suggests poor focus.
I’ve seen CVs where data engineers included front-end frameworks like Angular or React for roles that had no overlap with these skills. The truth is that you can only focus on a handful of technologies. When I see a CV like that, I know the candidate didn't spend enough time to go deep enough in any of the tools they had listed.
Hiring managers value clarity. Your CV should highlight what’s most relevant to the job, not everything you’ve ever done.
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How to Fix It 🧰
Tailor for Each Role: Use the job description to identify critical skills and focus your CV around them.
Emphasise Depth Over Breadth: Showcase a few well-developed competencies instead of listing dozens of tools.
Keep It Concise: Limit your CV to one or two pages, prioritising recent and relevant experience.
Example Rewrite 📝
❌ Before: “Skills: Python, Java, Kubernetes, React, Snowflake, Hadoop, Tableau, TensorFlow.”
✅ After: “Key Skills: Python, SQL, Snowflake, Data Pipeline Design, Query Optimisation, ELT Workflow Development.”
This curated list ensures clarity and keeps the focus on core competencies.
☑︎ Pitfall #3: Focusing on Tasks Instead of Achievements
Why This is a Problem ⚠️
A common mistake in CVs is listing tasks without context. Descriptions like “Designed ETL workflows” or “Developed dbt models” explain what you did but not why it mattered.
Hiring managers want to know your impact. Here are two questions I always ask myself and the candidate if I call them for an interview:
What was your role in this project?
Did your workflows save time, reduce costs, or enable better decision-making?
This is especially important in data engineering. Metrics like scalability, efficiency, and cost savings are critical. However, metrics must be authentic. If you inflate or fabricate numbers, expect tough questions during interviews.
A CV that focuses on outcomes stands out because it shows you understand the bigger picture. You’re doing more than executing tasks. You’re driving results.
How to Fix It 🧰
🎯 Connect Tasks to Impact: Frame your responsibilities in terms of outcomes.
📊 Use Metrics: Quantify your achievements with specific numbers.
❓ Understand the “Why”: Know how your work supports organisational goals.
Example Rewrite 📝
❌ Before: “Worked on ETL workflows for data integration.”
✅ After: “Built ETL workflows, reducing processing time by 40% and cutting cloud storage costs by $15,000 annually.”
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