Build and operate robust data pipelines for ingestion, cleaning, and transformation using Databricks, Airflow, or Dagster
Develop efficient ETL/ELT workflows in Python and SQL to support both batch and streaming workloads
Collaborate with ML and AI teams to deliver high-quality datasets for training, evaluation, and production features
Model and maintain structured data assets (Delta, Parquet, Iceberg) for reliability, versioning, and lineage tracking
Implement orchestration and monitoring — schedule jobs, track dependencies, and automate recovery from failures
Ensure data quality and compliance through validation frameworks, schema enforcement, and audit logging
Contribute to data platform evolution — evaluate tools, standardize best practices, and improve developer experience
Support performance and cost optimization across compute, storage, and orchestration systems

3–6 years of experience as a Data Engineer or ETL Developer in a production environment
Proficiency in Python and SQL; strong familiarity with Databricks, Spark, or equivalent big-data frameworks
Experience with workflow orchestration tools such as Airflow, Dagster, Luigi or Prefect
Deep understanding of data modeling, data warehousing, and distributed data processing
Knowledge of modern data lakehouse architectures (Delta, Parquet, Iceberg)
Familiarity with CI/CD, GitHub Actions, and data pipeline testing frameworks
Comfort working in a cross-functional environment with ML, product, and analytics teams

Data Engineer

Key skills