Design and build scalable, distributed data platform systems on AWS and Databricks, owning workspace architecture, governance, and lifecycle management. Implement infrastructure as code with Terraform, develop end-to-end data pipelines from ingestion to analytics and ML, and operate Amazon EKS clusters. Define SLIs/SLOs for platform reliability, lead incident response and on-call rotations, implement security and compliance controls, and mentor engineers on platform standards. This senior staff role requires 8+ years building large-scale cloud platforms, 5+ years in Data Platform/DevOps/SRE roles, expert AWS knowledge, and deep hands-on experience with Databricks Lakehouse (Unity Catalog, Delta Lake, MLflow), Terraform, CI/CD pipelines, and Python/Bash scripting.

Staff-level data engineer who designs, develops, and maintains scalable ETL/ELT pipelines using Databricks, PySpark, and Python for enterprise analytics and AI use cases. Builds curated data layers following Lakehouse Medallion architecture, develops Databricks-native applications (notebooks, dashboards, APIs), and optimizes Apache Spark workloads for performance and cost efficiency. Owns production data pipelines end-to-end, ensures data quality and reliability, and acts as a technical leader mentoring teams on data engineering best practices and modern Databricks capabilities.