Java Spark Engineer

Primary ResponsibilitiesArchitect and build scalable, fault-tolerant data pipelines using Apache Spark (Java)Lead design of batch and streaming ETL/ELT systems handling large data volumesDeep-dive performance tuning: partitioning strategy, memory management, shuffle/skew optimization, job cost reductionSet coding standards and lead code/design reviews across the teamDrive technical decisions on data architecture, storage formats, and pipeline orchestrationMentor mid-level and junior engineers; act as a technical escalation pointPartner with product, analytics, and platform teams to translate requirements into scalable systemsOwn production reliability — on-call ownership, incident response, root-cause analysis for pipeline failuresEvaluate and introduce new tools/frameworks where they improve the systemContribute to capacity planning and cost optimization for cluster infrastructureRequired QualificationsBachelor's or Master's degree in Computer Science, Engineering, or related field7+ years of professional Java development experience5+ years hands-on experience with Apache Spark in production environmentsExpert-level understanding of distributed systems: fault tolerance, data locality, shuffle mechanics, resource managementProven track record designing systems processing terabyte+ scale dataStrong SQL skills and deep familiarity with columnar storage formats (Parquet, ORC, Avro, Delta Lake/Iceberg)Experience with cluster managers (YARN, Kubernetes) and cloud-managed SparkProficiency with KafkaStrong grasp of CI/CD, containerization, and infrastructure-as-code practicesPreferred QualificationsExperience with Flink or other stream-processing frameworksFamiliarity with data governance, lineage, and quality frameworksExperience with workflow orchestration at scaleBackground in system design for multi-tenant or multi-region data platformsPrior experience leading a team or acting as a technical lead