• Lead end-to-end delivery of large-scale, scope-based data platforms and cloud migration projects, from initiation through closure. • Provide technical oversight and act as a bridge between engineering teams and client stakeholders, ensuring alignment on scope, timelines, and quality. • Oversee technical prerequisites and delivery readiness, including environment access, data ingestion setup, CDC analysis, SLAs, and validation processes. • Drive stakeholder communication, risk management, issue resolution, and milestone-based sign-offs. • Lead and support cross-functional engineering teams through clear prioritization, performance management, and delivery governance. • Own project financials, including forecasting, cost control, invoicing approvals, and scope change management. • Prepare and present regular project status reports, highlighting progress, risks, dependencies, and key metrics. • Support pre-sales and growth initiatives by contributing to estimations, delivery strategies, and follow-on phase planning. • Partner with recruiting teams to support hiring and onboarding for specialized technical roles.

• Research, recommend, and implement the optimal combination of technologies for the infrastructure dashboard data platform (e.g., databases, processing frameworks, orchestration tools) • Set up necessary data storage solutions with a focus on long-term scalability • Connect live data via API or other appropriate mechanisms from multi-domain sources • Collaborate with domain experts to understand Research, Development, Test & Evaluation (RDT&E) data being ingested • Ensure modeling controls for data abnormalities and structural differences • Build a robust data pipeline for loading and transforming data into a model appropriate for STF use, considering both short- and long-term data goals • Create and enforce data governance policies, including data quality standards, data lineage tracking, and metadata management • Communicate these policies to data analysts and stakeholders • Monitor and optimize database and/or data model performance and security • Conduct periodic testing of pipeline health and data validity • Work with data stakeholders and the data analyst team to ensure the background data and architecture meet the needs of requested visualizations and analyses • Create and maintain detailed documentation for data architecture, data models, pipeline jobs, and all related processes

• Lead the technical execution of the migration from Oracle and Boomi to Azure SQL and Azure Data Factory. • Guide architecture and design decisions across database migration, ETL migration, integration, validation, and deployment activities. • Serve as the main technical point of contact with the client. • Translate client requirements, priorities, and constraints into actionable technical direction for the team. • Coordinate the work of the database migration and ETL / ADF engineers. • Review and validate technical designs, source-to-target mappings, migration approaches, and implementation plans. • Provide hands-on technical guidance and troubleshooting support when needed. • Help resolve complex issues related to data conversion, pipeline design, performance, compatibility, validation, and reconciliation. • Track project timeline, deliverables, dependencies, risks, and open decisions. • Ensure the team is aligned on priorities, scope, quality expectations, and delivery commitments. • Communicate project status, risks, blockers, and key decisions to client stakeholders and internal leadership. • Support planning for testing, production cutover, rollback considerations, and post-migration stabilization. • Ensure technical documentation is created and maintained throughout the engagement. • Promote best practices for data migration, ETL design, security, performance, monitoring, and maintainability.

• Design, build, and maintain scalable and efficient data systems, leveraging big data technologies • Lead the data engineering team and manage the project roadmap, ensuring projects are delivered on time and within budget. • Collaborate with cross-functional teams, including data scientists, software engineers, and business stakeholders, to understand and meet their data needs. • Design and implement data pipelines to ingest, process, and store large and complex data sets, ensuring the reliability and performance of the data systems. • Implement data security and privacy controls, following industry best practices and company policies. • Monitor and optimize the performance of data systems, proactively identifying and resolving bottlenecks and issues. • Stay up-to-date with emerging trends and technologies in the field of data engineering, and evaluate their potential impact on the company. • Mentor and guide junior data engineers, providing technical and professional development opportunities to help them grow in their careers.