• Work with enterprise data architecture and systems. • Support IRS modernization initiatives. • Migrate legacy data structures. • Ingest data with ALC and RTF. • Design high-volume data pipelines. • Collaborate on enterprise integration and orchestration tools.

• Design, develop, and maintain ETL/ELT data pipelines using Databricks • Build and optimize data processing solutions in an Azure environment • Collaborate directly with the client to understand requirements and deliver technical solutions • Develop and optimize data transformations using PySpark • Work with Azure Data Factory and Jupyter Notebooks where applicable • Ensure data quality, reliability, and performance across data pipelines • Troubleshoot and resolve data-related issues • Contribute to the continuous improvement of data engineering solutions and best practices

• Own business problems, not just data requests • Deliver data-led solutions end to end • Own correctness and validation • Operate as the conduit between technical & commercial stakeholders

• Senior Data Engineer serves as an experienced individual contributor within a team, with the expectation that you will continue to develop your leadership, technical guidance, and mentoring skills. • With minimal oversight from leadership, you will design, build, and optimize scalable data solutions that support business and customer needs while ensuring projects meet scope, schedule, and delivery commitments. • Contribute to the long-term data strategy of the program, influence architectural decisions, and collaborate closely with software engineers, product managers, analysts, architects, and stakeholders to deliver reliable, secure, and scalable data platforms. • Serve as the primary data engineering lead for initiatives and utilize strong leadership and communication skills to drive improvements in data engineering processes, platform reliability, and engineering best practices. • Evaluate and recommend multiple technical approaches to solve complex data engineering and architecture challenges. • Design, develop, maintain, and optimize scalable data pipelines supporting both batch and real-time processing. • Design and implement robust ETL/ELT workflows that transform raw data into reliable, consumable datasets. • Build and maintain scalable data models, data warehouses, and cloud-native data architectures. • Generate data architecture recommendations and successfully implement approved solutions. • Ensure data quality, integrity, governance, lineage, security, and observability across data platforms. • Optimize database performance, query execution, storage strategies, and overall system scalability. • Diagnose and resolve production issues while implementing long-term improvements to increase system reliability and performance. • Collaborate with cross-functional teams to translate business requirements into scalable technical solutions. • Present technical designs, architecture diagrams, and implementation strategies to clients, stakeholders, partners, and engineering teams. • Champion data engineering best practices, coding standards, automation, and operational excellence. • Mentor junior engineers through technical guidance, code reviews, design discussions, and knowledge sharing. • Lead small projects or serve as the technical lead for data engineering initiatives when needed. • Effectively communicate technical challenges, risks, and progress with engineering teams, leadership, clients, and stakeholders. • Participate in technical interviews and contribute to hiring decisions.