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Data Engineer (Airflow / Snowflake)

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Summary

Remote Data Engineer building and maintaining Apache Airflow pipelines that load data into an AWS S3 data lake and Snowflake, ingesting from APIs, databases and files. Core stack: Python, SQL, Airflow, Snowflake, dbt; focus on performance tuning, cost, and data quality. B2B contract up to 160 PLN/h net + VAT.

Industry: IT

Rate: up to 160 pln/h net + VAT, B2B

Location: 100% remote

About role:

We are seeking a skilled and experienced Data Engineer specializing in Apache Airflow and Snowflake. The ideal candidate will have a strong background in designing and maintaining data pipelines, with a focus on performance optimization and data quality. Essential skills include proficiency in Python and SQL, alongside hands-on experience with data modeling and cloud data storage solutions.

Main Responsibilities:

  • Design, build, and maintain Apache Airflow DAGs in Python that load data into the Data Lake (S3) and Snowflake.

  • Build pipelines that ingest data from APIs, databases, and files.

  • Model and transform data with dbt.

  • Tune SQL queries and Snowflake performance and cost.

  • Set up monitoring, alerting, and error handling for pipelines.

  • Protect data quality through testing and validation, and keep documentation current.

Key Requirements:

  • 3+ years of experience as a Data Engineer.

  • Apache Airflow: strong knowledge of operators, sensors, hooks, TaskFlow API, XCom, pools, backfills, and idempotent DAG design.

  • Python: strong skills, with clean, testable code and pytest.

  • SQL: advanced level, including window functions, CTEs, and query optimization.

  • Snowflake: hands-on experience with warehouses, stages, external volumes, COPY INTO.

  • dbt: models, tests, macros, snapshots, and incremental models.

  • AWS S3: Data Lake organization, partitioning, file formats (Parquet, JSON, CSV), and IAM policies.

  • Git and CI/CD experience.

  • English at B2 level or higher.

Nice to Have

  • Docker and Kubernetes.

  • Open table formats: Apache Iceberg or Delta Lake.

  • Data quality and observability tools such as Great Expectations or Soda.

  • Dimensional modeling (Kimball).

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