
Data Engineer
3 days ago
**Responsibilities**:
- Design, implement, and maintain data pipelines for the migration of data from on-premises systems to Hadoop-based platforms.
- Develop and optimize data processing jobs using Spark, Hive, and Python.
- Manage job orchestration and scheduling using Control-M, ensuring timely and accurate data delivery.
- Collaborate with cross-functional teams to understand data requirements and deliver efficient solutions.
- Perform code quality checks and peer reviews to ensure best practices, maintainability, and adherence to coding standards.
- Ensure end-to-end operational stability of data pipelines by proactively identifying and resolving bottlenecks, failures, and data quality issues.
- Ensure data quality through rigorous cleaning and validation processes.
- Documented data flow processes, transformation logic, and framework usage to support onboarding and troubleshooting.
**Requirements**:
- Proficiency in Python and SQL.
- Strong experience with Hadoop ecosystem tools (Hive, Spark).
- Worked extensively with transformation components, mapping development, and workflow orchestration in Informatica/DataStage.
- Experience with job scheduling and monitoring using Control-M.
- Familiar with pipeline-as-code concepts and using Jenkins files for automation of build and deployment processes.
- Solid understanding of database systems including Teradata, Oracle, and SQL Server.
- Ability to analyze and troubleshoot large-scale data processing systems.
- Experience in the banking or financial services industry.
- Knowledge of data warehousing concepts and star/snowflake schema design.
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