Engineer, Data and AI Governance

Company Summary EchoStar is reimagining the future of connectivity. Our business reach spans satellite television service, live-streaming and on-demand programming, smart home installation services, mobile plans and products. Today, our brands include Boost Mobile, DISH TV, Gen Mobile, Hughes and Sling TV. Department Summary Our Technology teams challenge the status quo and reimagine capabilities across industries. Whether through research and development, technology innovation or solution engineering, our team members play a vital role in connecting consumers with the products and platforms of tomorrow. Job Duties and Responsibilities Candidates must be willing to participate in at least one in-person interview, which may include a live whiteboarding or technical assessment session.   The Data & AI Governance engineer bridges the gap between high-level data governance policies and actual code execution. Working directly within Line of Business (LOB) delivery pods, you will act as the working-level governance partner. You are responsible for the technical implementation of data and AI governance, specifically leveraging but not limited to the Databricks (Unity Catalog) & Snowflake (Horizon Catalog) ecosystems to ensure the delivery of high-quality, safe, and compliant data products. Rather than acting as a traditional auditor, you will "shift left" by integrating automated quality gates and lineage mapping directly into the development lifecycle, serving as the first line of defense for data and AI model safety.   What Success Looks Like: Data & AI Governance Implementation: Partner with cross-functional teams to implement Data & AI Governance policies and standards as an overarching Governance Layer on Databricks / Snowflake & other Data Platforms Embedded Pod Integration: Serve as the dedicated governance and technical data quality resource within agile delivery pods. Actively participate in stand-ups, sprint planning, and retrospectives to detect compliance and quality risks early, preventing deployment bottlenecks Rapid Risk Assessment & Triage: Manage the initial intake and classification of new data and AI use cases. Perform rapid risk assessments and "T-shirt sizing" to determine the appropriate level of scrutiny, ensuring low-risk initiatives move to production at high velocity while high-risk models receive robust validation Consultative Guidance & Liaison: Translate complex corporate governance requirements, data classification rules, and testing evidence mandates into clear, actionable technical instructions for delivery teams. Bridge the communication gap between business stakeholders and technical engineering pods Continuous Process Optimization: Monitor the practical performance of governance controls within active pods. Partner with leadership to identify friction points, reduce administrative overhead, and continuously automate the "Governance Engine" to accelerate delivery cycles Automation & Monitoring:Own the build-out of automated governance monitoring and observability — including statistical distribution, variance, and drift checks across Bronze/Silver/Gold data layers — leveraging SQL/Python engineering to deliver scalable, repeatable quality assurance at the platform level Skills, Experience and Requirements Core Skills and Competencies (What You'll Bring) Coding & Automation: Python, Infrastructure as Code (Terraform), and workflow orchestration tools Data & Systems: Cloud platforms, data catalogs, lineage tracking, and access management Hands-on experience managing governance features in Databricks & Snowflake for cataloging, fine-grained access control, and end-to-end lineage tracking) and Databricks Genie Strong understanding of Lakehouse architectures, Delta Lake, Glue and Horizon Catalog as well as data pipelines to inspect, and validate underlying data quality. Proven ability to translate high-level compliance policies (e.g., data privacy, security classifications) into concrete technical