Senior Data Engineer

Job Description

- Architect and optimize distributed data processing pipelines leveraging PySpark for high-throughput, low-latency workloads. - Utilize the Apache big data stack (Hadoop, Hive, HDFS) to orchestrate ingestion, transformation, and governance of massive datasets. - Engineer fault-tolerant, production-grade ETL frameworks ensuring seamless scalability and system resilience. - Interface cross-functionally with Data Scientists and domain experts to translate analytical needs into performant data solutions. - Enforce rigorous data quality controls and lineage mechanisms to uphold auditability and regulatory compliance. - Contribute to core architectural design, implement clean and modular Python/Java code, and drive performance benchmarking at scale. Required Skills : - 5-7 years of experience. - Strong hands-on experience with PySpark for distributed data processing. - Deep understanding of Apache ecosystem (Hadoop, Hive, Spark, HDFS, etc.) - Solid grasp of data warehousing, ETL principles, and data modeling. - Experience working with large-scale datasets and performance optimization. - Familiarity with SQL and NoSQL databases. - Proficiency in Python and basic to intermediate knowledge of Java. - Experience in using version control tools like Git and CI/CD pipelines. Nice-to-Have Skills : - Working experience with Apache NiFi for data flow orchestration. - Experience in building real-time streaming data pipelines. - Knowledge of cloud platforms like AWS, Azure, or GCP. - Familiarity with containerization tools like Docker or orchestration tools like Kubernetes.