Data Engineer Job Description Template (Free, 2026)

Data Engineer [Company Name] - [City, State] / Remote Full-Time | Data Platform Team | Reports to: Head of Data About the Role: We are looking for a Data Engineer to build and maintain the data infrastructure that enables our analysts, data scientists, and product teams to make high-quality, timely decisions. You will design ingestion pipelines, model data for analytical use, and ensure that our data platform is reliable, observable, and easy to work with. You care about data quality as much as throughput, and you communicate technical trade-offs clearly to stakeholders. Responsibilities: - Design and build batch and streaming data pipelines that ingest data from product databases, third-party APIs, and event streams - Model data in our cloud data warehouse (Snowflake / BigQuery / Redshift) using dimensional modeling and dbt - Maintain and improve pipeline orchestration using Apache Airflow or Prefect - Implement data quality checks and alerting to catch upstream issues before they reach dashboards - Collaborate with Data Scientists to design and maintain feature stores and model input pipelines - Optimize query performance and manage compute costs in the data warehouse - Document data models, lineage, and pipeline dependencies using a data catalog - Participate in on-call rotation for data pipeline reliability - Work with Analytics Engineers and BI developers to ensure datasets meet reporting needs - Contribute to data governance practices including PII handling and access controls Required Qualifications: - 3+ years of experience building production data pipelines - Strong SQL skills including window functions, CTEs, and performance tuning - Proficiency in Python for ETL/ELT pipeline development - Experience with at least one cloud data warehouse (Snowflake, BigQuery, or Redshift) - Familiarity with workflow orchestration tools such as Apache Airflow or Prefect - Understanding of data modeling concepts including star schemas and normalized models - Experience with version control (Git) and treating data pipelines as code - Knowledge of data quality patterns and testing frameworks such as Great Expectations or dbt tests Nice to Have: - Experience with Apache Spark or Flink for large-scale distributed processing - Familiarity with streaming platforms such as Apache Kafka or Amazon Kinesis - Experience with dbt (data build tool) for transformations - Background in building or maintaining a feature store for ML use cases - Knowledge of data catalog and lineage tools such as DataHub, Amundsen, or Collibra Compensation and Benefits: - Base salary: [Salary Range] (see benchmarks below) - Equity: [Stock options / RSUs] - Health, dental, and vision insurance - [X] days PTO plus public holidays - Remote-friendly work environment - Annual learning budget including conference attendance - Home office stipend About [Company Name]: [Write 2-3 sentences about your company, data maturity, and what the engineer will be building.] [Company Name] is an equal opportunity employer committed to a diverse and inclusive workplace.