• 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