• Lead end-to-end data migration from legacy systems to cloud platforms, including data mapping, transformation, and validation (Salesforce-centric). • Analyze and convert unstructured, document-heavy data into structured, analytics-ready formats using Document AI. • Implement data quality processes (cleansing, deduplication, reconciliation) to ensure accuracy and completeness post-migration. • Support phased migration and system cutover with minimal operational disruption. • Design, build, and maintain MuleSoft integrations, including API development for real-time and batch data exchange. • Apply data transformation logic (e.g., DataWeave) and troubleshoot integration/data flow issues across systems. • Contribute to Salesforce Data 360 implementation, including data harmonization, identity resolution, and unified profiles. • Ensure proper data ingestion, lineage, metadata management, and traceability across the ecosystem. • Ensure compliance with data governance, privacy, and security requirements (e.g., PII). • Collaborate in Agile teams; support testing, documentation, and audit readiness across migration and integration efforts.

• Design and build the AWS-native data foundation behind enterprise AI applications • Lead the design and evolution of the knowledge graphs and ontologies powering AI's reasoning, retrieval, and explainability • Align enterprise data into a coherent, queryable graph with clear provenance across structured, semi-structured, and unstructured sources • Curate grounding corpora, eval datasets, and retrieval benchmarks for LLM-based features • Build ingestion and transformation pipelines in Python and SQL using AWS services • Author infrastructure as code in CloudFormation and apply AWS best practices • Partner with security and platform teams to integrate data access with enterprise identity and access policies • Set the design direction for data and semantic modeling across the team

• Design and develop solutions addressing new features, interoperability, vulnerabilities, and system limitations. • Evaluate current capabilities, anticipate future needs, and partner with stakeholders and vendors to deliver enhancements. • Research, design, and implement enterprise security solutions that align with business strategy and protect organizational data. • Support and influence change management and security initiatives across assigned areas. • Integrate security into business processes and assess risks associated with new applications and technologies. • Provide recommendations to mitigate risk and support business objectives. • Serve as a security subject matter expert to engineering, architecture, and development teams. • Perform advanced troubleshooting and provide support, including participation in on-call rotations. • Deliver timely reports and findings to management and leadership. • Recommend and interpret security policies, standards, and best practices. • Identify risk gaps and develop appropriate solutions. • Stay current on emerging threats and trends, assessing impact and integrating improvements into security strategies. • Drive process improvements that enhance quality and efficiency across teams. • Provide guidance and technical direction on complex initiatives. • Develop and maintain correlation searches and alerts using diverse log sources. • Optimize detection and response through automation, orchestration, and advanced analytics. • Monitor and ensure the effectiveness of prevention, detection, and response capabilities. • Collaborate with cybersecurity engineering and threat intelligence teams to improve security tools and processes. • Contribute to the development of cybersecurity procedures. • Mentor and train team members. • Demonstrate initiative and accountability in achieving results.

• Design, build, and optimize data pipelines and ETL workflows • Develop scalable data models, data structures, and data integrations • Work with modern cloud platforms including Azure Synapse, Data Factory, Data Lake, and Databricks • Ensure data quality, governance, security, and compliance • Partner with business stakeholders, analysts, and data scientists • Contribute to or lead complex, cross-functional data initiatives • Improve platform reliability through monitoring, validation, and automation • Create clear documentation, runbooks, and data lineage • Mentor junior engineers (Senior-level roles)