Senior Data Engineer responsible for designing, building, and maintaining large-scale data pipelines and infrastructure using Big Data technologies, SQL, Java, and Linux environments, while driving DataOps best practices for reliability and performance.
About the role
Key Responsibilities
Design, develop, and optimize scalable data pipelines and ETL processes using Big Data frameworks (e.g., Hadoop, Spark) to ingest, transform, and store large volumes of structured and unstructured data.
Write high-performance SQL queries and Java code to support data extraction, transformation, and loading across multiple data sources and target systems.
Collaborate with cross-functional teams to define data models, data quality standards, and metadata management practices.
Implement and maintain DataOps workflows, including CI/CD pipelines, automated testing, and monitoring for data pipeline health and performance.
Ensure data security, compliance, and governance across all data assets, working closely with data stewards and security teams.
Requirements
5+ years of experience as a Data Engineer or similar role, with a strong background in Big Data technologies.
Proficiency in SQL and Java, with experience building production-grade data pipelines.
Deep knowledge of Linux system administration and scripting (Bash, Python).
Hands‑on experience with DataOps practices, CI/CD, and monitoring tools (e.g., Airflow, Jenkins, Prometheus).
Strong analytical skills, problem‑solving mindset, and ability to work in a fast‑paced, collaborative environment.