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)
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