How to get hired
Nearly every data engineer job post asks for three or more years. That is not a wall, it is a hint: companies want people who have already broken a pipeline in production and fixed it. The work before "Data Engineer" can be almost anything that touches SQL.
The feeder jobs
- ETL or SQL developer. People who wrote stored procedures, SSIS packages or Informatica mappings for a bank or insurer are the classic data engineers of tomorrow. Move the same logic into Spark or dbt and you have the story.
- Data analyst. If you are the analyst who keeps automating your own reports, ask to own the pipeline behind them. Six months of that is real data engineering experience.
- Backend developer. Python or Java developers who already handle queues, APIs and databases switch easily, often onto Kafka and streaming work.
- Services-firm trainee. Large IT services firms put some freshers onto Azure, AWS or Snowflake data projects after training. If you join one, say clearly in your first month that you want a data project, and get the relevant certification while you wait on the bench.
What interviews test
- SQL, hard version. Deduplicate a table keeping the latest record per customer. Find users active on three consecutive days. Explain why a query with a correlated subquery is slow.
- Spark or PySpark. What a shuffle is, why one task takes forty minutes when the rest take two (data skew), when to broadcast a small table, partitioning and file sizes.
- Data modelling. Star schema versus a wide table, fact and dimension tables, slowly changing dimension Type 2 and how you'd implement it.
- Pipeline design. "Design a daily load of 50 million transactions from a bank's core system." They listen for incremental loads, idempotent reruns, late-arriving data, data quality checks and what happens when the source is down.
- Orchestration and cloud. How an Airflow DAG is structured, retries and backfills, and the managed services on the cloud the team uses.
Certifications employers name
Certifications matter more here than in data science, because services firms and consultancies staff projects by them.
- Microsoft Fabric Data Engineer Associate (DP-700). The older Azure Data Engineer Associate (DP-203) retired on 31 March 2025, so recruiters who still ask for "DP-203" will accept DP-700.
- AWS Certified Data Engineer – Associate.
- Databricks Certified Data Engineer Associate, and Snowflake's SnowPro Core, for teams on those platforms.
- Google Cloud Professional Data Engineer, for GCP shops.
Pick the one that matches the stack in the jobs you want; holding all four helps less than one plus a real project.
Portfolio
Build one pipeline end to end: pull a public API or dataset on a schedule, land raw files in cloud storage, transform with PySpark or dbt, load into a warehouse, add tests that fail loudly, and put the repo on GitHub with an architecture diagram. Watch your cloud bill; set a budget alert on day one.
First titles
Associate Data Engineer, Data Engineer I, ETL Developer, Big Data Developer, Azure Data Engineer, Analytics Engineer.
What the work is like
The job is mostly plumbing, and good plumbing is invisible. When it breaks, the morning dashboards are wrong and everyone knows. Many teams run an on-call rotation for pipeline failures, and month-end and financial-year close (March) are heavy in banks and finance teams.
Pay is among the best in the data family once you are past three years, and the jump to Senior Data Engineer is where it moves most. The ladder goes Data Engineer, Senior, Lead, then Data Architect or Principal Engineer. Platform migrations (Hadoop to cloud, on-premise warehouses to Snowflake or Databricks) keep demand steady.