About the Data Engineer role
A Data Engineer builds and maintains the infrastructure that moves data through a company. They design pipelines that ingest data from applications, APIs, and third-party sources, transform it into usable shape, and load it into warehouses or lakes where analysts, scientists, and applications can rely on it.
The role is closer to software engineering than to analysis. A strong data engineer writes production-grade code, thinks about reliability and cost, and treats data quality as an engineering problem with tests, monitoring, and alerts. In many companies, data engineers also own the data platform itself: orchestration, warehouse performance, access controls, and the tooling other data roles depend on.
In your posting, name your warehouse, orchestration tool, and cloud provider, and describe the scale of data involved. State whether the engineer will build pipelines for analytics, power customer-facing features, or both. Candidates evaluate data engineering roles largely on the maturity of the stack.
Data Engineer job description template
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Job brief
Join us as a Data Engineer and build the pipelines the rest of the company depends on. You will move data from applications, APIs, and third parties into our warehouse, shape it into something usable, and keep the whole thing running when volumes and schemas change underneath you.
You will do well here if you write production code rather than scripts, treat data quality as an engineering problem with tests and alerts behind it, and care that the numbers people act on are right.
Responsibilities
- Design, build, and maintain scalable ETL and ELT pipelines from internal and external data sources
- Model, document, and optimize datasets in the data warehouse for analytics and product use
- Write clean, tested, production-grade code for data ingestion and transformation
- Monitor pipeline health and data quality, and resolve failures and anomalies quickly
- Optimize warehouse performance and manage storage and compute costs
- Implement data validation, testing, and observability across pipelines
- Collaborate with analysts and data scientists to understand data needs and deliver reliable datasets
- Manage data access, security, and compliance requirements with the platform team
- Evaluate and integrate new tools that improve the data platform
- Document how the data flows so the next engineer does not have to reverse engineer it
Requirements and skills
- Has built and operated data infrastructure in a production environment
- Strong SQL, including making a slow query fast on a large table
- Writes Python, or another language commonly used for pipelines, to a production standard
- Hands-on with a cloud data warehouse such as Snowflake, BigQuery, or Redshift
- Has run an orchestration tool such as Airflow, Dagster, or Prefect
- Comfortable with data modeling, including dimensional models and slowly changing dimensions
- Familiar with at least one major cloud platform
- A computer science or engineering degree, or equivalent practical experience
Nice to have
- Experience with dbt and modern ELT workflows
- Experience with streaming technologies such as Kafka or Kinesis
- Familiarity with Spark or other distributed processing frameworks
- Experience with infrastructure as code tools such as Terraform
- Exposure to data governance, cataloging, or privacy tooling
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