Hadoop Solutions Developer

Job Summary

We are seeking an experienced Hadoop Solutions Developer to design, build, and operate large-scale data processing pipelines and analytics platforms on Hadoop and related big-data ecosystems.

Key Responsibilities

• 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

Required Qualifications

• 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

Preferred Qualifications

• 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