Senior Data Platform Engineer - Assistant Vice President

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.