Data Engineer
Core
Design, build, and maintain scalable data pipelines, ETL processes, and analytics infrastructure to support AI model training and business decision-making.
Role type
Senior Data Engineer (Analytics Engineering focus)
Builds
Scalable data pipelines, ETL processes, analytics infrastructure, feature stores, and data governance frameworks.
Domain
Artificial Intelligence / Data Infrastructure / Cloud Data Warehousing
Deliverable
production ML models | infrastructure
Required skills
Python, SQL, dbt, cloud platforms (AWS, GCP, Azure), data warehousing (Snowflake, BigQuery, Redshift, Clickhouse), data pipeline design, ETL development, data quality automation, data governance, metadata management, data lineage.
Preferred skills
MLOps, feature engineering, containerization (Docker, Kubernetes), DevOps practices, CI/CD pipelines, infrastructure-as-code (Terraform), self-service data platforms.
Technologies
Python, SQL, dbt, AWS, GCP, Azure, Snowflake, BigQuery, Redshift, Clickhouse, Docker, Kubernetes, Terraform
Responsibilities
Design and maintain scalable data pipelines and ETL processes; automate data quality checks and validation; optimize data storage, retrieval, and query performance; collaborate with ML teams on model training and feature stores; enforce data governance and lineage standards.
Seniority
Mid-Senior, hands-on IC
