Senior Data Platform Engineer

Senior Data Platform Engineer We are seeking a highly skilled and motivated Senior Data Platform Engineer, AVP (Python/PySpark, Microsoft SSIS, SQL Server, ETL & Database Development) to design, develop, enhance, and support robust, scalable, and high-performance data processing solutions. The ideal candidate will combine strong hands-on expertise in Python/PySpark, SQL Server, ETL/data pipelines, Microsoft SSIS, and large-scale data platforms, with experience troubleshooting production data applications, optimizing database performance, and working across enterprise data environments. The candidate will be working independently on complex assignments while collaborating with engineering, infrastructure, and business teams. Responsibilities: Data Pipeline & ETL Development: Design, develop, test, and support scalable ETL/data-processing pipelines using Python/PySpark, Microsoft BI/SSIS, SQL, and related technologies. SQL Server Development: Develop and optimize complex SQL queries, stored procedures, functions, data transformations, and database objects for high-volume applications. Database Performance: Identify and resolve query and database performance issues through execution-plan analysis, indexing, query optimization, statistics, and appropriate database design techniques. Large-Scale Data Processing: Develop and support solutions processing large data volumes across SQL Server and distributed data platforms. Data Architecture & Modeling: Apply relational, dimensional, and data-warehouse modeling principles to build scalable and maintainable data solutions. Production Support & Troubleshooting: Analyze failed jobs, data-quality issues, performance problems, and production incidents; perform root-cause analysis and implement sustainable resolutions. Job Scheduling & Automation: Develop and support enterprise batch-processing workflows using scheduling/orchestration platforms such as AutoSys. Big Data Platforms: Work with Hadoop ecosystem technologies including HDFS, Hive and related distributed-processing technologies. Data Quality & Reconciliation: Implement validation, reconciliation, exception handling, logging, monitoring, and data-quality controls within data pipelines. Code Quality: Participate in code reviews and enforce development standards for SQL, Python/PySpark, ETL, and data-processing components. DevOps: Use Git-based source control and participate in CI/CD processes; work with container technologies such as Docker/Kubernetes where applicable. Security: Follow secure data-development practices, access-control requirements, encryption standards, and organizational security policies. Technical Collaboration: Work closely with application developers, infrastructure teams, business teams, and other technology groups to design and deliver solutions. Continuous Improvement & AI: Evaluate new technologies and leverage approved AI-assisted development tools to improve code quality, troubleshooting, refactoring, and developer productivity. Appropriately assess risk when business decisions are made, demonstrating consideration for the firm's reputation and safeguarding the firm, its clients, and assets while complying with applicable laws, rules, regulations, and policies. Recommended Qualifications: 4+ years of relevant experience in data engineering, application development, database development, or related technology roles. Strong hands-on experience with Python and PySpark for large-scale data processing and ETL development. Strong proficiency in SQL/T-SQL and relational database technologies, preferably Microsoft SQL Server. Experience developing and supporting complex SQL queries, stored procedures, database objects, and high-volume data-processing solutions. Strong understanding of SQL Server performance tuning, including query execution plans, indexing, statistics, joins, locking/blocking, and query optimization. Experience with Microsoft BI/SSIS or comparable enterprise ETL technologies. Experience working with large datasets and distributed data platforms such as Hadoop/HDFS/Hive. Strong understanding of ETL architecture, data warehousing, dimensional modeling, and relational/3NF modeling. Experience implementing data-quality checks, reconciliation, audit controls, exception handling, and production monitoring. Experience with enterprise scheduling/orchestration tools such as AutoSys. Experience with Git, CI/CD, and modern development practices. Familiarity with Docker/Kubernetes is preferred. Familiarity with NoSQL technologies such as MongoDB is beneficial. Strong analytical, troubleshooting, communication, and problem-solving skills. Ability to work independently on complex technical assignments while collaborating effectively across teams. Experience mentoring or providing technical guidance to other developers Education: Bachelor's degree/University degree or equivalent experience Citigroup Inc