Data Engineer

• Develop, optimize, and maintain large-scale data pipelines using Databricks, Spark, and Delta Lake
• Implement scalable ETL/ELT patterns for structured, semi-structured data
• Optimize performance, costs, and reliability of Databricks jobs and clusters
• Perform Redshift administration tasks including cluster management, and database administration
• Build and optimize Redshift models, tables, and complex SQL queries to support analytics and reporting
• Monitor and improve Redshift performance, storage efficiency, and query optimization
• Develop and maintain data pipelines powering engineering analytics dashboards and metrics
• Work with stakeholders to define and compute engineering KPIs (deploy frequency, MTTR, quality metrics, etc.)
• Own ingestion and processing pipelines for behavior analytics and product telemetry
• Provide L2/L3 support for existing data pipelines, ensuring high availability and rapid incident resolution
• Lead root-cause analysis (RCA) and implement preventive measures for recurring issues
• Implement monitoring, logging, alerting, and observability best practices across data systems
• Partner with the Data Engineering Lead, Product Management, Analytics, Architecture, and Engineering teams to deliver data solutions that meet evolving business needs
• Translate technical problems into actionable design documents, code, and operational processes
• Support downstream consumers with data availability, quality, and documentation

Senior Data Engineer

Senior Data Engineer