Data Engineer

• Design, develop, test, and maintain scalable data pipelines and data-integration solutions. 

• Build ETL/ELT processes for structured, semi-structured, and unstructured data. 

• Integrate data from enterprise applications, databases, APIs, cloud platforms, and third-party systems. 

• Contribute to the technical design and implementation of end-to-end data solutions. 

• Develop reusable and maintainable Python, PySpark, Spark SQL, and SQL components. 

• Implement data-quality checks, validation rules, reconciliation processes, logging, and exception handling. 

• Optimize Spark workloads, SQL queries, partitioning, and data-processing performance. 

• Develop and maintain data models, data dictionaries, mappings, and technical documentation. 

• Implement data-security, privacy, governance, and role-based access requirements. 

• Support workflow orchestration, scheduling, monitoring, alerting, and recovery processes. 

• Contribute to CI/CD pipelines, automated testing, version control, and deployment processes. 

• Troubleshoot data-pipeline failures, performance issues, and data-quality problems. 

• Collaborate with data architects, business SMEs, analysts, data scientists, product teams, and DevOps teams. 

• Participate in sprint planning, backlog refinement, technical estimation, and delivery activities. 

• Take ownership of assigned data-engineering work from development through deployment and production support. 

• Evaluate new technologies and recommend improvements to data-engineering processes and platform performance. 

• Follow coding, testing, documentation, security, and reusable-development standards. 

• Participate in operational support activities, including occasional off-hours support. 

Basic Qualifications

• Master’s or Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related field, with 5–8 years of relevant professional experience.