Data Engineer Job Description
A Data Engineer builds and maintains the infrastructure that collects, stores, and processes data so analysts and scientists can use it. They design data pipelines, manage data warehouses, ensure data quality, and make raw data available in structured, queryable formats. The role is plumbing for data — invisible when done well, painfully obvious when it breaks.
All roles Data Engineer
What does a Data Engineer do?
On a typical day, a Data Engineer builds or modifies ETL/ELT pipelines that move data between systems, writes SQL and Python to transform raw data into usable datasets, monitors pipeline health and handles failures, and designs schema changes to accommodate new data sources. They work with stakeholders to understand data requirements, optimize warehouse performance, manage access controls, and build tools that make data self-service for analysts.
Data Engineer responsibilities
- Design, build, and maintain data pipelines that extract, transform, and load data from multiple sources
- Build and manage data warehouse schemas (star schema, snowflake, or dimensional modeling)
- Write SQL and Python for data transformation, cleaning, and validation
- Ensure data quality through monitoring, validation checks, and anomaly detection
- Manage data warehouse performance through partitioning, clustering, and query optimization
- Implement data governance including access controls, data cataloging, and lineage tracking
- Build and maintain real-time or near-real-time streaming data pipelines where needed
- Collaborate with analysts and data scientists to understand data requirements and deliver usable datasets
- Document data sources, transformations, schemas, and pipeline dependencies
- Automate data operations — pipeline scheduling, error handling, alerting, and recovery
Essential requirements
- Strong SQL skills including complex joins, window functions, CTEs, and query optimization
- Experience with at least one data warehouse platform (Snowflake, BigQuery, Redshift, Databricks)
- Proficiency in Python for data processing and pipeline development
- Experience building and orchestrating ETL/ELT pipelines (Airflow, dbt, Dagster, Prefect)
- Understanding of data modeling concepts (dimensional modeling, normalization, data vault)
- Familiarity with cloud storage (S3, GCS, Azure Blob) and file formats (Parquet, ORC, Avro)
Preferred qualifications
- Experience with streaming platforms (Kafka, Kinesis, Pub/Sub, Flink)
- Familiarity with dbt for analytics engineering and data transformation
- Knowledge of data quality frameworks (Great Expectations, Soda, Monte Carlo)
- Experience with data cataloging and metadata management tools (DataHub, Amundsen, Atlan)
- Exposure to infrastructure-as-code for data infrastructure (Terraform, CloudFormation)
Core skills
Technical / professional skills
- SQL (advanced queries, optimization, schema design)
- Data warehouses (Snowflake, BigQuery, Redshift, Databricks)
- Programming (Python with pandas, PySpark, or Polars)
- Pipeline orchestration (Airflow, dbt, Dagster, Prefect, Luigi)
- Cloud platforms and managed data services (AWS Glue, GCP Dataflow, Azure Data Factory)
- Streaming systems (Kafka, Kinesis, Pub/Sub, Flink)
- Data file formats and storage (Parquet, ORC, Avro, S3, Delta Lake)
- Data quality and monitoring (Great Expectations, dbt tests, custom validation)
Soft skills
- Data quality obsession — catching bad data before it reaches decision-makers
- Systematic debugging — tracing data through pipelines to find where transformations break
- Stakeholder communication — translating business questions into technical data requirements
- Documentation discipline — writing schema docs and pipeline descriptions that others can maintain
- Pragmatism — balancing ideal data architecture with what is achievable given timelines
Experience and education guidance
Junior Data Engineers (0-2 years) build and modify existing pipelines under supervision. Mid-level engineers (2-5 years) design new pipelines, manage warehouse performance, and own data quality for specific domains. Senior Data Engineers (5+ years) architect the overall data platform, make technology choices, and set data engineering standards across the organization.
Data Engineers come from software engineering, database administration, analytics, and academic backgrounds. Strong SQL and Python skills are more important than a specific degree. Cloud data certifications (AWS Data Analytics, GCP Professional Data Engineer) can validate skills but are not universally required.
What to include in this job description
Specify the data warehouse technology, the pipeline orchestration tool, whether the role involves streaming or batch processing, the scale of data (GB, TB, PB), and how many data sources the engineer will work with. Mention whether the role is building from scratch or maintaining existing infrastructure.
Common job description mistakes for this role
Conflating Data Engineer with Data Analyst (building infrastructure vs. analyzing data), requiring a PhD for a role that primarily involves SQL and pipeline orchestration, not mentioning the actual data warehouse technology, and describing the role as data science when it is data engineering.
How to customize this job description
After generating a Data Engineer JD, add the specific warehouse, orchestration tool, and cloud platform your team uses. If the role involves streaming data (Kafka, real-time dashboards), emphasize that. If it is primarily analytics engineering (dbt, modeling for analysts), adjust the focus accordingly.
Frequently asked questions
What does a Data Engineer do?
A Data Engineer builds and maintains the infrastructure that collects, stores, and processes data. They design data pipelines, manage data warehouses, ensure data quality, and make data available in structured formats for analysts and scientists to use.
What is the difference between a Data Engineer and a Data Analyst?
A Data Engineer builds the infrastructure (pipelines, warehouses, schemas) that stores and processes data. A Data Analyst queries that data to answer business questions and build reports. Engineers build the plumbing; analysts use it to find insights.
What skills does a Data Engineer need?
Core skills include advanced SQL, Python, data warehouse platforms (Snowflake, BigQuery), pipeline orchestration (Airflow, dbt), data modeling, cloud platform experience, and understanding of data quality and governance.
Do I need a degree to become a Data Engineer?
Not necessarily. Strong SQL and Python skills, hands-on experience with data warehouses and pipelines, and a portfolio of data projects can demonstrate readiness. Many Data Engineers transition from software engineering, analytics, or database administration roles.
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