Data Engineer CV: example, skills and writing tips

Data engineer CV example

The example below shows how the advice in this guide fits together on one page. The person, the companies and all the figures are invented: use the structure, not the content, and replace every detail with your own.

Notice what makes this CV useful to a recruiter: every line says what was built, from which sources, with which technology and at what scale. The rest of this guide explains how to build each section in the same way.

Job title and professional summary

Choose a title that reflects your actual stack

A bare “Data Engineer” says little. When it reflects your experience, add the technologies or environment that define your profile:

Data Engineer – Python, SQL, AWS
Data Engineer – Spark and Databricks
Cloud Data Engineer – Azure
Senior Data Engineer – cloud data platforms
Junior Data Engineer – Python and SQL

Avoid vague titles such as “Data Expert” when your specialism is clearly data engineering. A precise title lets recruiters, and the search tools they use, place you quickly.

Data engineer, analytics engineer, data scientist: which title fits?

Data roles overlap and their scope varies between employers, so always check the job description and pick the title that describes what you actually do.

Professional summary example: experienced data engineer

The professional summary (often called a personal profile in the UK) is three to five lines that say what you build, with which stack and at what scale.

Data engineer with 6 years’ experience designing and industrialising data pipelines. Python and SQL development, Airflow orchestration, Spark processing and AWS architecture, on platforms handling several million records a day.

Professional summary example: junior data engineer

Junior data engineer with first experience from a [12-month] work placement on Python and SQL pipelines. Built ETL jobs, orchestrated them with Airflow and loaded a data warehouse used by BI teams.

Adapt these examples to your real situation. “Passionate about data and new technologies” tells a recruiter nothing about your level: replace it with what you have built.

Data engineer CV skills by area

Do not list every tool on the market. Select the skills you genuinely use and that match the role, then phrase them so a recruiter can see what you built with them.

A skills line such as this gives little information:

Python · SQL · Spark · AWS · Airflow

This one is far more useful:

Built Python and SQL pipelines orchestrated with Airflow, loading data from 12 business sources into an AWS data warehouse.

Describing your experience and achievements

A good pipeline description answers four questions: where does the data come from, how is it processed, with which technology, and what is it used for? For each line, follow this order where possible:

context → your own action → tools or method → scope → result

For example: “For an online retailer’s BI team (context), I rebuilt the daily sales pipeline (action) in PySpark on Databricks (tool) for 80 million records a day (scope), cutting run time from 2.5 hours to 45 minutes (result).” The figures in this sentence are illustrative.

Too vague

Created ETL pipelines
Data processing
Cloud
Monitoring

More informative

Illustrative figures.

Built Python ingestion pipelines loading CRM, ERP and e-commerce data into the data warehouse every day
Developed Spark jobs transforming several hundred million rows before exposure to BI teams
Ran daily batch pipelines in Airflow with retries and failure alerts
Added dbt tests on critical models

The second version shows the sources, the processing, the scale and your role in reliability. Add figures when they are accurate and not confidential: number of sources, pipelines, rows or GB/TB processed, processing frequency, number of users, countries or entities.

Achievement examples to adapt

Replace [X] and [Y] with your own verified figures, or leave the line out if you cannot support it.

• Reduced the run time of the main batch from [X] to [Y] by [optimising Spark jobs / repartitioning data].
• Maintained [X] production pipelines integrating [Y] internal and external sources.
• Processed around [X] million events a day from [source] into [warehouse].
• Reduced [Snowflake/compute] costs by [X]% by optimising warehouses and queries, counting only savings due to your own work.
• Made finance data available before [time] every day for [X] reports, with monitoring of the agreed availability target.
• Reduced data incidents reported by users from [X] to [Y] a month with automated quality checks.

Tech stack: show how you used it

“SQL: expert” or a five-dot rating bar tells a recruiter nothing. Name the technologies you have actually used and say what for:

Python: file and API ingestion, transformations, pipeline automation
SQL: data warehouse models and quality checks
Airflow: DAG creation, dependencies, retries and alerts
dbt: SQL models, tests and documentation

For cloud platforms, avoid “AWS / Azure / GCP” if your experience is not equal on all three. Name the services you used:

AWS: S3, Glue, Lambda, Redshift and IAM on a data platform
Azure: Data Factory, Databricks, Data Lake Storage and Synapse
GCP: BigQuery, Cloud Storage, Pub/Sub and Dataflow

Say whether your pipelines are batch, streaming or both: “pipeline” covers very different architectures. Do not claim streaming expertise if you only consumed existing Kafka topics, and do not list a tool you only met in a tutorial: you should be able to discuss it in a technical interview.

Education and certifications

Data engineers come from computer science, software engineering, data, mathematics, statistics or engineering backgrounds. Use the format qualification – specialism – institution – year:

[Degree] – [specialism]
[Institution] — [year]

Cloud or platform certifications (for example from AWS, Microsoft, Google Cloud or Databricks) can support your profile. List only certifications you have actually obtained, with their exact name, the issuer, the date and, where relevant, the validity period. Online courses should not take more space than your experience.

If you are experienced, education can take less space than your most recent roles.

Making your data engineer CV easy to read for ATS

IT services companies, recruitment agencies and data teams often search for candidates by precise technologies. The aim is not to “beat” or trick an applicant tracking system (ATS): it is to make sure your key information is extracted correctly and easy to find.

• explicit job titles (“Data Engineer”, not only “Data profile”);
• clearly identifiable employers or clients, and dates;
• experience organised role by role or project by project;
• technologies written as text, not only as logos or rating bars;
• tool names spelled as employers write them (Apache Airflow, Snowflake, dbt);
• certifications stated with their exact name.

“Data Engineer AWS – Python, Airflow, Spark and Redshift” is far more precise than “Versatile data profile”, but only use the technologies that match your real experience.

Tailoring your data engineer CV to each job

Data engineering roles vary widely depending on the stack. Your background does not change, but you can adapt the title, the summary, the technologies you bring forward and the projects you detail.

Before applying to an AWS, Databricks or analytics engineering role, it helps to see which elements of the job description your CV already covers and which are missing. CVReader compares your CV with a job description and highlights the points of correspondence.

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Junior data engineer or career changer

With little professional experience, the same principle applies: be precise about what you have actually built.

• Students and recent graduates: describe work placements, apprenticeships and university projects like real jobs, with sources, tools and volumes.
• Personal projects: a complete pipeline says more than a list of courses, for example a daily API extract stored in PostgreSQL, transformed with dbt and orchestrated with Airflow. Keep it in a separate “Projects” section so it is not confused with professional experience.
• Career changers: software developers, BI developers, data analysts or database administrators can bring forward the SQL, Python, ETL or automation work they have genuinely done.

BI developer moving into data engineering. Three years’ experience writing SQL transformations and ETL jobs for a reporting data warehouse; personal project automating API ingestion with Python and Airflow ([link]).

Trained or working in France?

If your data background is French, a few adjustments make your CV clearer to English-speaking recruiters.

French qualifications

Keep the French name of your qualification and add a short explanation:

• Diplôme d’ingénieur: French engineering degree awarded by an accredited engineering school (école d’ingénieurs), usually after five years of higher education.
• MIAGE – Méthodes informatiques appliquées à la gestion des entreprises: French university programme combining computer science and business management.

There is no automatic equivalence between these qualifications and British, American or other degrees, so do not present them as equivalent.

Diplôme d’ingénieur – [specialism] (French engineering degree)
[School] — [year]

French terms on an English CV

Translate the context, not the words. An “alternance” is a work-study contract or apprenticeship; an “ESN” (entreprise de services du numérique) is an IT services company or consultancy, and a “mission” is a client assignment. Name the client’s sector when you can.

Mistakes to avoid and final checklist

Only listing technologies

Explain what you actually built with them.

Listing three clouds without detail

In-depth experience of one cloud is more credible than a generic list.

Confusing data engineering and data science

On machine learning projects, state your exact contribution, for example the pipelines feeding the models.

Giving no sense of scale

Volumes, sources and frequencies place your projects.

Using “expert” too easily

Describe how you used a tool rather than rating yourself.

Presenting tutorial tools as professional skills

Separate professional experience from personal projects.

Forgetting reliability

Monitoring, data quality and incident handling are part of the value you bring.

Checklist before you apply

• My title states my role and, if useful, my main stack.
• My summary fits in three to five lines and says what I build, with which stack and at what scale.
• Each role shows sources, processing, technologies, volumes and, where possible, a result.
• Every figure is accurate, verifiable and not confidential.
• Each technology listed is one I have actually used, with its context.
• Personal projects are separated from professional experience.
• Employers, clients and dates are easy to find, and skills are written as text.
• I have brought forward the stack and focus that match the job.

Data engineer CV: frequently asked questions

What skills should I put on a data engineer CV?

The technologies you have actually used, plus your pipeline, orchestration, modelling, data quality, cloud and deployment skills, each with a few words of context.

Should I put Python and SQL in my CV title?

It can help if these technologies are central to your profile and to the jobs you target, as long as the title stays short and readable.

Should I include data volumes?

Yes, when they are accurate and not confidential. Rows, GB or TB, sources, pipelines and frequency show the scale of your work.

Should I add a GitHub link?

It is especially useful for junior profiles, if your public projects genuinely show data engineering work.

Is a data engineer the same as an analytics engineer?

The roles overlap. Analytics engineers usually focus on transformations, analytical modelling and BI use, while data engineers work more broadly on pipelines and infrastructure.

Is a data engineer resume different from a data engineer CV?

“Resume” is the usual American term; “CV” is used in the UK and much of the world. For a job application, the advice in this guide applies to both.