Responsibilities :

- Design, build, and optimize ETL/ELT pipelines for batch and streaming data

- Develop and maintain data warehouses and data lakes (e.g. , Snowflake, BigQuery, Redshift)

- Collaborate with data scientists, analysts, and product teams to understand data needs

- Ensure data quality, reliability, and observability across pipelines

- Build and manage orchestration workflows (e.g. , Airflow, Prefect, Dagster)

- Implement best practices for data modeling, partitioning, and performance tuning

- Monitor and troubleshoot data infrastructure issues in production

Requirements :

- 5+ years of experience in data engineering or a related role

- Proficiency in SQL and Python (or Scala/Java)

- Experience with cloud platforms (AWS, GCP, or Azure)

- Hands-on with big data tools : Spark, Kafka, dbt, or similar

- Familiarity with data warehousing concepts and dimensional modeling

- Strong understanding of CI/CD and version control (Git)

Nice to Have :

- Experience with real-time/streaming pipelines (Kafka, Flink, Kinesis)

- Exposure to ML pipelines or MLOps tooling

- Knowledge of data governance and data catalog tools (e.g. , Atlan, Alation)