A data engineer builds and runs the systems that move data from apps, databases and files into a place where analysts and models can use it. The work is mostly SQL, Python, pipelines, a cloud platform and a data warehouse. It suits people who like building reliable systems more than presenting results.

What employers ask for right now

Seniority. The market wants experienced people. Senior roles are the largest group, and entry-level roles are rare.

Workplace. Remote and hybrid roles are both common. Fully onsite roles are less common.

Pay. The block above shows current pay ranges from postings that publish pay. For a wider official benchmark: the US Bureau of Labor Statistics has no separate data engineer category. Its closest one, data scientists, shows a median pay of $120,230 in May 2025 and 35% projected growth for 2025–35 (checked October 2026).

Tools employers name. dbt, Airflow, Snowflake and the three big clouds (AWS, GCP, Azure) are among the most named tools. Spark, Databricks, BigQuery, CI/CD and Git are also common. SQL and Python do not appear in the skill list only because our labeller does not extract them. In practice almost every data engineer posting asks for both.

Who hires. Product companies, banks and many consultancies. Consultancies hire for client projects across several clouds.

Step-by-step: how to get in

Because entry-level roles are rare, the usual path is to enter a nearby role first and then move across. The steps below follow that path.

1. Learn SQL well. Joins, window functions, CTEs, aggregation and query plans. This is the base of every later step. Free resources and the documentation of PostgreSQL are enough. Plan for a few months of regular practice.
2. Learn Python for data. Reading files and APIs, working with JSON, writing tests, and packaging code. You do not need machine learning.
3. Get a job that touches data. Data analyst, BI developer, backend developer and DevOps engineer are the common starting points. Each one gives you part of the job: analysts know SQL and the business, backend developers know code and databases, DevOps engineers know cloud and CI/CD.
4. Learn the modern stack. Build one project with dbt models on top of a warehouse (Snowflake, BigQuery or Databricks), and schedule it with Airflow. These names are among the most common in data engineer postings. You can practise at no cost: dbt Core and Airflow are open source under the Apache 2.0 license; the BigQuery sandbox needs no credit card (10 GiB storage, 1 TiB of queries a month); Databricks Free Edition is free for personal use; and a Snowflake trial runs for 30 days.
5. Pick one cloud and go deep. AWS, GCP and Azure are all common in postings. Learn storage, IAM, a managed warehouse and how to deploy a pipeline with CI/CD.
6. Consider one cloud certificate, as a study plan. The official options: - AWS Certified Data Engineer – Associate (exam DEA-C01): $150 (checked October 2026), 130 minutes, 65 questions. AWS suggests 2–3 years in data engineering and 1–2 years on AWS. - Google Cloud Professional Data Engineer: $200 (checked October 2026), 2 hours, 40–50 questions, valid for 2 years, renewal exam $100 (checked October 2026). Google recommends 3+ years in industry with 1+ year on Google Cloud. There are no formal prerequisites.

Both vendors aim these exams at people who already work in the field. Very few postings ask for a cloud certificate. Treat it as a structured way to learn, not as a door opener.

1. Build a portfolio that looks like real work. Build one end-to-end project that uses the tools from step 4: ingest a public API daily, load raw data, transform it with dbt, add tests and data quality checks, run it on a schedule, and write a short README on design choices and costs.
2. Move inside your company first. Ask to own a pipeline, a dbt project or a data quality job. An internal move is often the shortest way to the title, because your employer already trusts you.

Entry level

At entry level you maintain pipelines that others designed. You fix failed jobs, add new sources, write dbt models and SQL tests, and learn the warehouse.

What employers ask for: solid SQL and Python, Git, basic cloud use, and some proof that you have built a pipeline. A degree in computer science, maths or a related field is common in requirements; BLS lists a bachelor's degree as typical entry education for its closest category.

What to learn next: orchestration (Airflow), data modelling (star schemas, slowly changing dimensions), and how to read a query plan.

Mid level

At mid level you design pipelines yourself and own them in production. You choose between batch and streaming, decide how to model tables, and set up monitoring and alerts.

What changes: you are expected to work without close review, handle cost and performance, and talk to analysts and product teams about what they need. CI/CD and Spark start to matter here, because larger data volumes and team workflows need them.

What to learn next: Spark or Databricks for large data, infrastructure as code, and data contracts between teams.

Senior and staff level

Senior roles are the largest group in postings. A senior data engineer owns a platform or a domain, sets standards, reviews others' work and mentors juniors. Staff engineers shape the data architecture across teams and make build-or-buy decisions.

What employers ask for: years of production experience, often across more than one cloud, plus warehouse and lakehouse design. LLMs appear often in postings: teams now want data engineers who can prepare data for AI features, such as document pipelines and embeddings.

What to learn next: platform design, governance and access control, cost management, and how to explain trade-offs to leadership.

Differences by country

• United States: the largest market for data engineer roles.
• United Kingdom and Canada: smaller markets than the US, with fewer open roles.
• India and Brazil: many roles are with consultancies or remote teams serving US clients, for example "Senior Data Engineer (Remote in Brazil)". English and overlap with US working hours often matter as much as the stack.

Common mistakes

• Waiting for an entry-level data engineer opening. Entry-level roles are rare, so this can take a long time. Enter through an analyst, BI or backend role instead.
• Taking a cloud exam before you have used the cloud. AWS suggests 1–2 years on AWS for its data engineer exam, and Google recommends 1+ year on Google Cloud. Very few postings ask for a cloud certificate.
• Portfolio projects with no tests or scheduling. A notebook that runs once does not show data engineering. Show a pipeline that runs daily, fails loudly and recovers.
• Ignoring dbt. It is one of the most named tools in postings, and it is easy to learn if you already know SQL.
• Skipping the analytics engineer title. Many postings, such as "Senior Analytics Engineer", ask for the same SQL, dbt and warehouse skills. It is a good first target for analysts.

FAQ

Can I become a data engineer without a degree? Yes, but it is harder. Most employers list a degree, and BLS gives a bachelor's degree as the typical entry education for the closest official category. A portfolio of working pipelines and experience in a nearby role can replace it at many companies.

Do I need a certificate to get a data engineer job? Seldom. Very few data engineer postings on RoleFalcon ask for a cloud certificate. A certificate helps you learn a cloud, but employers hire on skills and experience.

How much does a data engineer earn? The live block above shows current pay ranges from postings on RoleFalcon. For an official benchmark, the US Bureau of Labor Statistics gives a median of $120,230 (May 2025, checked October 2026) for data scientists, its closest category.

Are there entry-level data engineer jobs? Few. Entry-level roles are rare; most postings want some experience. Most people move in from data analyst, BI, backend or DevOps roles.

Which tools should a data engineer learn first? SQL and Python first. Then the tools that employers name most often - dbt, Airflow, one cloud (AWS, GCP or Azure) and one warehouse such as Snowflake, BigQuery or Databricks.

Can a data engineer work remotely? Often. Remote and hybrid roles are both common. Fully onsite roles are less common.