Primary Responsibilities
• Architect and build scalable, fault-tolerant data pipelines using Apache Spark (Java)
• Lead design of batch and streaming ETL/ELT systems handling large data volumes
• Deep-dive performance tuning: partitioning strategy, memory management, shuffle/skew optimization, job cost reduction
• Set coding standards and lead code/design reviews across the team
• Drive technical decisions on data architecture, storage formats, and pipeline orchestration
• Mentor mid-level and junior engineers; act as a technical escalation point
• Partner with product, analytics, and platform teams to translate requirements into scalable systems
• Own production reliability — on-call ownership, incident response, root-cause analysis for pipeline failures
• Evaluate and introduce new tools/frameworks where they improve the system
• Contribute to capacity planning and cost optimization for cluster infrastructure
Required Qualifications
• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
• 7+ years of professional Java development experience
• 5+ years hands-on experience with Apache Spark in production environments
• Expert-level understanding of distributed systems: fault tolerance, data locality, shuffle mechanics, resource management
• Proven track record designing systems processing terabyte+ scale data
• Strong SQL skills and deep familiarity with columnar storage formats (Parquet, ORC, Avro, Delta Lake/Iceberg)
• Experience with cluster managers (YARN, Kubernetes) and cloud-managed Spark
• Proficiency with Kafka
• Strong grasp of CI/CD, containerization, and infrastructure-as-code practices
Preferred Qualifications
• Experience with Flink or other stream-processing frameworks
• Familiarity with data governance, lineage, and quality frameworks
• Experience with workflow orchestration at scale
• Background in system design for multi-tenant or multi-region data platforms
• Prior experience leading a team or acting as a technical lead