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# Senior ETL Developer - Assistant Vice President

- **Company:** Citi
- **Location:** Tampa, FL, United States
- **Experience:** Expert
- **Salary:** $96,960.0 - $145,440.0
- **Contract:** Permanent contract
- **Skills:** Software Applications, Big Data, Computer Programming, Information Engineering, Extract Transform Load (ETL), Software Debugging, Memory Management, Apache Hadoop, Hadoop Distributed File System, Apache Hive, Systems Analysis, Python (Programming Language), Performance Tuning, Software Engineering, Sqoop, Data Processing, Apache Spark, Caching
- **Published:** October 20, 2026
- **Apply:** https://citi.wd5.myworkdayjobs.com/2/job/Tampa-Florida-United-States/Senior-ETL-Developer---Assistant-Vice-President_26969321-1/apply

## About the Role

* 5-8 years of relevant experience in Software Development
 * Experience in systems analysis and programming of software applications
 * Experience in managing and implementing successful projects
 * Working knowledge of consulting/project management techniques/methods
 * Ability to work under pressure and manage deadlines or unexpected changes in expectations or requirements

Education:

 * Bachelor's degree/University degree or equivalent experience

## Description

Senior ETL Developer will be responsible for designing, implementing, and optimizing distributed data processing jobs to handle large-scale data in Hadoop Distributed File System(HDFS) using Apache Spark and Python. This role required deep understanding of data engineering principles, proficiency in Python and hands-on experience with Spark and Hadoop ecosystems. Developer will collaborate with data engineers, analysts, and business stakeholders to process, transform and drive insights and data driven decisions., Data Processing and Transformation:

 * Design and Implement of Spark applications to process and transform large datasets in HDFS.
 * Develop ETL Pipelines in Spark using Python for data Ingestion, cleaning, aggregation, and transformations.

Performance Optimization:

 * Optimize Spark jobs for efficiency, reducing run time and resource usage.
 * Finetune memory management, caching, and partitioning strategies for Optimal performance
*

Data Engineering with Hadoop and Spark:

 * Load data from different sources into HDFS, ensuring data accuracy and integrity.
 * Integrate Spark Applications with Hadoop frameworks like Hive, Sqoop etc.
 * Testing and debugging:
 * Troubleshoot and debug Spark Job failures, monitor job logs, and Spark UI to Identify Issues.