About this role
Job Requirements
- Design and develop robust data ingestion and ETL pipelines for batch and workflow-based processing requirements.
- Build, optimize, and maintain data processing solutions using Apache Spark, PySpark, and Apache NiFi.
- Develop, schedule, monitor, and manage workflows in Apache Airflow to ensure reliable orchestration.
- Work on the Cloudera Data Engineering platform and the broader Hadoop ecosystem, including HDFS and Hive.
- Write efficient Hive SQL and Python code for data transformation, validation, and operational automation.
- Support, troubleshoot, and optimize production data pipelines for performance, reliability, and scalability.
- Collaborate with data architects, analysts, platform teams, and business stakeholders to understand requirements and deliver data solutions.
- Follow engineering best practices for code quality, version control, documentation, deployment, and monitoring.
Work Experience
- 8+ years of relevant experience in data engineering, big data platforms, and production pipeline delivery.
- Strong hands-on experience in designing data pipelines using NiFi, Spark/PySpark, Hive SQL, and Python.
- Good understanding of Hadoop concepts, HDFS storage patterns, data partitioning, and distributed processing.
- Experience managing Airflow DAGs, dependencies, retries, monitoring, and failure handling.
- Ability to identify pipeline bottlenecks and perform performance tuning across ingestion, transformation, and orchestration layers.
Strong problem-solving mindset with the ability to support critical production workloads.