Role Description We are looking for a Principal Data Engineer to sit at the intersection of data engineering and applied data science. You will own the design, development, and operation of the platforms and pipelines that power our data science capabilities — ensuring data flows reliably from source systems through to analysis, and business consumption. This role is roughly 75% data engineering and 25% data science and is ideal for someone who builds with engineering rigor but thinks with a data science mindset; someone who is energized by building platforms that make AI real in an organization. The right candidate is curious by nature — you explore out-of-the-box ideas and stay current with the fast-moving AI/Machine Learning (ML) landscape. You’ll work directly with: - Business analysts - Product owners - Business end-users - Engineering and application teams - Data/platform engineering teams A consultative communication style is critical as shared outcomes across technology and business are the expectation. Qualifications - Bachelor’s degree in computer science, engineering, mathematics, or a related field, OR 7+ years of equivalent verifiable experience, skillset, and record of accomplishment - Experience in a Principal or Senior Data Engineer role with direct involvement in ML platform or Data Science work - Proficiency in an analytics/BI tool such as Power BI Requirements - Modern data stack technologies — Databricks (strongly preferred), Snowflake, Spark - Inbound/outbound transportation of data with APIs and FTPs - MPP databases such as Databricks, Snowflake, BigQuery, Teradata, or Azure Synapse - Cloud platforms — AWS, Azure, or GCP - Python and SQL - Building and deploying ML models (classification, regression, forecasting, NLP, or similar) - Familiarity with ML frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow - MLflow or similar tools for experiment tracking, model registry, and deployment - Understanding of feature engineering, model evaluation, and common ML failure modes - Strong understanding of data modelling techniques (Kimball, Data Vault) and distributed systems - Familiarity with feature stores, training pipelines, and batch/real-time inference architectures Benefits - 401K company match up to 4% - Paid Time Off - Medical Insurance options including FSA & HSA - Vision Insurance - Dental insurance - Employee Assistance Programs - Team Member Referral Program - Tuition Reimbursement - Wellbeing Program - Career development opportunities

• The Mid-Level Full Stack Data Engineer is responsible for building and supporting data-driven features across backend services and thick client desktop applications. • Develop and maintain data ingestion and transformation pipelines. • Implement backend logic to process, validate, and analyze structured and semi-structured data. • Contribute to RESTful API development and service-layer integrations. • Support integration of processed data into thick client desktop applications (e.g., Electron-based platforms). • Assist in database schema design, data modeling, and query optimization. • Develop UI-integrated data features such as dashboards, validation results, and analytics views. • Write unit and integration tests to validate data processing logic. • Participate in sprint planning, backlog refinement, and demos. • Collaborate with senior engineers and architects to implement scalable and maintainable data solutions.

• Design and implement enterprise data architectures including Data Lakes, Data Warehouses, Data Fabric, and MDM solutions. • Build scalable data pipelines and data integration frameworks. • Define data governance, quality, lineage, and security standards. • Enable AI/ML initiatives through robust data engineering and architecture practices. • Collaborate with business and technology teams to develop data strategies and roadmaps. • Optimize data platforms for performance, scalability, and reliability.

• 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