Senior Data Engineer – Hadoop

• Design, develop, and operate end-to-end big-data pipelines on Hadoop, ingesting data from a diverse mix of relational, file-based, streaming, and API-driven sources.
• Build robust ETL/ELT workflows using Apache Spark, Hive, Pig, and Sqoop, with strong attention to data quality, idempotency, error handling, and recoverability.
• Develop high-throughput streaming data pipelines using Kafka, Spark Streaming, or Flink, and integrate them with downstream analytical and operational systems.
• Optimize Spark and MapReduce jobs through careful tuning of partitioning, memory, serialization, and skew handling to meet demanding SLAs at minimal cost.
• Design and maintain data models and storage layouts on HDFS, Hive, HBase, and modern lakehouse formats (Parquet, ORC, Delta, Iceberg, Hudi) to balance flexibility and performance.
• Implement data governance, lineage, and quality controls in collaboration with data governance and security teams.
• Build robust monitoring, alerting, and logging strategies for big-data pipelines, including job-level SLAs and proactive failure detection.
• Partner with data scientists and analysts to deliver curated, reliable, and well-documented datasets that accelerate their work.
• Automate pipeline orchestration using Airflow, Oozie, or similar workflow engines, with clean dependency management and clear ownership boundaries.
• Continuously evaluate and adopt new technologies in the big-data and cloud ecosystem (EMR, Databricks, Snowflake, BigQuery) where they offer meaningful improvements.
• Lead performance reviews and architecture audits of existing pipelines, proposing concrete refactoring and optimization initiatives.
• Document data architectures, schemas, pipeline behaviors, and operational runbooks in a way that makes the platform supportable as the team scales.
• Mentor junior engineers and contribute to the team’s engineering standards and best practices.

• Bachelor’s degree in Computer Science, Engineering, or a related technical discipline.
• Five or more years of professional experience designing and operating big-data pipelines on Hadoop.
• Strong hands-on expertise with Apache Spark (Scala, Python, or Java) in production environments.
• Solid experience with Hive, HDFS, Sqoop, HBase, and the broader Hadoop ecosystem.
• Hands-on experience with streaming data platforms such as Kafka, Spark Streaming, or Flink.
• Strong SQL skills and experience working with both relational and NoSQL data stores.
• Experience with workflow orchestration tools such as Airflow or Oozie.
• Solid understanding of distributed systems concepts, including partitioning, replication, and fault tolerance.
• Strong scripting skills in Python or Shell.
• Excellent troubleshooting, debugging, and documentation skills.

• Experience operating Hadoop on cloud platforms such as AWS EMR, Azure HDInsight, or Databricks.
• Familiarity with modern lakehouse formats (Delta, Iceberg, Hudi).
• Exposure to data governance tooling such as Apache Atlas or Collibra.
• Experience with Kubernetes-based data platforms (Spark-on-K8s, Trino).
• Hands-on experience with CI/CD and infrastructure-as-code in data engineering workflows.

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