• Act as the primary strategic partner to client business owners, aligning technical data solutions with corporate goals while championing reliability and "clean data" for all consumers. • Drive the development of complex, large-scale data architectures and multi-year technology roadmaps, balancing technical innovation with cost efficiency and operational stability. • Oversee "team-of-teams" delivery performance, ensuring the quality and resilience of high-impact data pipelines while managing capacity planning and team velocity. • Lead the triage and resolution of critical platform incidents, taking full ownership of root-cause prevention and maintaining transparent communication with clients during recovery. • Implement robust governance frameworks and compliance controls, acting as the authoritative lead on risk mitigation and the primary technical contact for regulatory matters. • Champion an organisational culture of knowledge sharing, representing the firm at industry events and establishing best practices across communities of practice. • Influence long-term organizational health by driving platform efficiency, managing engineering budgets, and leading strategic hiring and retention initiatives.

• Build the infrastructure required for optimal ELT of data using cutting-edge AWS ‘big data’ technologies (Glue, Data-Lake, Lambda functions, etc.) and Snowflake technologies. • Identify, design, and implement internal process improvements by optimizing data delivery and enhancing existing frameworks for greater scalability. • Write, do a unit test, and deploy software code conforming to SDLC processes by leveraging the agile methodology. • Implement the software architecture for a given project by creating features and tasks with appropriate estimations using Azure DevOps. • Keep abreast of current trends in system architectures and development environments and support proof-of-concept initiatives to evaluate new technologies. • Provide periodic software code reviews to determine if coding standards and proper architecture design are being adhered to. • Work closely with Technical Support and data-engineering teams to promptly address support tickets for data operations. • Perform development project management task: Collaborate with Business Owners on scoping and requirements for each iteration. • Manage and maintain the progress of development projects. • Work with the security team for disaster recovery preparedness and ensure HITRUST compliance is maintained at all data processing levels. • Ensure data is retained only for the duration of its useful life and per compliance and contractual requirements.

• Lead and ensure timely completion of data cleansing activities • Manage data collection for manual and constructed objects • Collaborate with IT teams to define and implement value mappings • Create and maintain master data lists for assigned objects • Provide business insights for data extraction and transformation from legacy systems • Define and align data migration scope with country stakeholders • Ensure data readiness and quality throughout the migration lifecycle • Validate that data is fit for purpose and aligned with stakeholder requirements • Review and formally approve data upload files before and after loading • Perform manual data loads where required • Support dual maintenance activities • Execute and approve data validation scripts • Act as SPoC during defect management and hypercare phase

• Data Architecture: Design, develop, and implement scalable, secure, and efficient data solutions that meet the needs of the organization. • Data Modeling: Create and maintain logical and physical data models to support business intelligence, analytics, and reporting requirements. • Pipeline Engineering: Design, build, and optimize ETL (Extract, Transform, Load) processes and data pipelines to ensure smooth and efficient data flow from various sources. • Data Integration: Integrate diverse data sources, including APIs, databases, and third-party data, into a unified data platform. • Performance Optimization: Monitor and optimize the performance of data systems and pipelines to ensure low latency and high throughput. • Data Quality and Governance: Implement data quality checks, validation processes, and governance frameworks to ensure the accuracy and reliability of data. • Collaboration: Partner closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions that meet their needs. • Documentation: Maintain comprehensive documentation of data architectures, models, and pipelines for ongoing maintenance and knowledge sharing. • Training: You'll train and collaborate with teammates effectively in data engineering best practices • Technical Influence/Leadership: Recommends policy changes and establishes department-wide procedures. Uses extensive experience and knowledge to resolve complex problems. • Monitor and manage production environment to deliver data within defined SLAs