• Participation and representation in a client facing Data Governance Committee, including making recommendations, guiding decisions and recommendations within the scope of Client, State and Federal data governance policies, practices, and standards relevant to data management • Ensure and maintain to the highest standards the quality and security of data within program engagement through adherence to processes and procedures for access controls and monitoring of data activities and use to protect data integrity, including ensuring compliance for the preservation and protection of Protected Health Information (PHI) and Protected Identification Information (PII) • Collaborate with stakeholders in the design, testing, implementation and ongoing maintenance and support activities of data services • Assist the Client in solving data-related issues by managing data corruption or mapping data between program areas • Maintain internally established data quality reporting metrics, evaluate, and identify issues/corrections and coordinate and implement data management best practices • Identify data assets, lineage, and business rules within data domains to ensure data element continuity and avoid data conflict • Manage data design, creation and management of database architecture and data repositories • Maintain accurate and current content within the data management plan including all diagrams, workflows, and all other data related documentation • Conduct and comply with data reviews and auditing to ensure adherence to current standards, policies and design of the database architecture and repositories • Ensure documentation and processes are followed, including Architectural Board approvals and other necessary approvals, for all data architecture changes

• Design and operate the cloud data infrastructure powering AI initiatives. • Architect production-scale data lakes on AWS. • Build real-time ingestion and observability pipelines. • Own the vector search and embedding layers that feed RAG systems and autonomous agents.

• Lead the architectural vision for a next-generation data layer designed specifically for Agentic AI. • Design the end-to-end blueprint for a modern data layer that seamlessly integrates structured, unstructured, and relational (Graph) data for AI agents. • Oversee the health, security, and performance optimization of our data clusters (Snowflake/Kinetica), ensuring 99.9% availability for mission-critical AI workflows. • Act as the 'Face of Engineering' for the customer. Lead discovery workshops, manage technical expectations, and align the architectural roadmap with their business objectives. • Establish benchmarks for data latency and retrieval accuracy, ensuring the data layer can keep pace with the real-time demands of agentic execution.

Role Description - Data Analysis & Insight Generation: - Analyze large and complex datasets to extract meaningful insights that drive business outcomes. - Communicate findings and recommendations through reports, dashboards, and presentations. - Data Engineering & Preparation: - Clean, preprocess, and transform raw data for analysis and modeling. - Collaborate with data engineering teams to ensure data availability and quality. - Collaboration with Stakeholders: - Work closely with product managers, engineers, and business leaders to understand requirements and deliver data-driven solutions. - Translate business problems into analytical frameworks. - A/B Testing & Experimentation: - Design and analyze A/B tests to measure the impact of product changes and marketing campaigns. - Provide statistical rigor in experimentation and decision-making. - Research & Innovation: - Stay up-to-date with the latest developments in data science, machine learning, and AI. - Propose innovative approaches and solutions for complex problems. - Other duties as assigned Qualifications - Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related field. - 5+ years of experience in data science or a related field. - Hands-on experience with data analysis, machine learning, and statistical modeling. - Proficiency in Python, R or similar technologies for data analysis and modeling. - Strong experience with data manipulation libraries (e.g., Pandas, NumPy) and machine learning libraries (e.g., Scikit-Learn, TensorFlow, PyTorch). - SQL proficiency for data extraction and transformation. - Knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud) and big data technologies (e.g., Spark, Hadoop) is a plus. Benefits - Medical, Dental & Vision Benefits - 401(k) Savings Plan with Company Match - Flexible Planned Paid Time Off - Generous Sick Leave - Inclusive & Welcoming Environment - Purpose-Driven Culture - Work-Life Balance - Commitment to Community Involvement - Employer-Paid Parental Leave - Employer-Paid Short-Term Disability - Remote Work Flexibility