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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Engineer, Data and AI Governance - **Company:** ECHOSTAR - **Location:** Englewood, CO, United States - **Experience:** Experienced - **Salary:** $96,250.0 - $137,500.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Cloud Computing, Information Systems, Information Engineering, Identity and Access Management, Python (Programming Language), Metadata Repositories, Scrum Methodology, SQL Databases, Technical Data Management Systems, Data Classification, Snowflake, Data Layers, Data Lakes, Infrastructure Automation Frameworks, Information Technology, Data Lineage, Data Management, Terraform, Data Pipelines, Databricks - **Published:** August 16, 2026 - **Apply:** https://www.techcareers.com/job.asp?id=3355987691&tx=JP5751FFL&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * 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 designs and configuration rules * Modern Governance Principles: Strong understanding of "shift-left" governance - integrating quality, compliance, and risk checks directly into the early stages of the software/data development lifecycle rather than treating them as a final gate * Comfort operating in fast-paced, pod-based agile environments (Sprints, Stand-ups, Retrospectives) while maintaining an uncompromising focus on "Quality Gates" * Strong analytical skills to evaluate data/AI use cases, distinguish between varying risk levels in real-time, and apply proportional governance controls, * Minimum Education: Bachelor's Degree in Computer Science, Data Engineering, Information Systems, or a related technical field * Minimum Experience: 2-4 years of experience in Data Engineering, Data Analyst, Product Management or Technical Consulting with an emphasis on data management or governance * Required Technical Skills: Must have at least 2 years of experience with: * Databricks (Unity Catalog) and/or Snowflake (Horizon Catalog) governance administration * SQL, Python, Infrastructure as Code (Terraform), and workflow orchestration tools. * Implementation of "shift-left" automated data quality, privacy, and risk governance controls ## Description 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 ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Infrastructure as Code: The Developer's Secret Weapon](https://www.wearedevelopers.com/videos/1221-infrastructure-as-code-the-developer-s-secret-weapon) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads) ## Related Articles - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)