Data Management Engineer

Your Job

As a Data Management Engineer, you will be a key individual contributor responsible for building and scaling the technical capabilities that enable trusted, well-governed, and discoverable data across the enterprise. Sitting within the Data Management organization, this role blends hands-on engineering with data governance, metadata, and quality enablement.

You will own the technical configuration and evolution of our Alation data catalog, develop automation that integrates metadata and governance into data ingestion workflows, and help embed agentic and AI-assisted capabilities that strengthen data quality, transparency, and stewardship. This role works closely with data governance, data quality, and data architecture leads, as well as the Data Platform team, to ensure data management is delivered as a cohesive, end-to-end capability.

• Hands-On Builder: You enjoy writing code, configuring platforms, and automating processes rather than defining concepts alone.
• Detail-Oriented: You have a passion for ensuring data accuracy, consistency, and long-term sustainable data management.
• Systems Thinker: You understand how metadata, governance, quality, and architecture work together to support analytics and data products.
• Collaborative: You work well across disciplines, value shared ownership, and help reinforce a "one team" mindset within Data Management.
• Continuous Learner: You are curious about emerging tools, patterns, and AI-driven approaches to data management.

• Data Catalog & Metadata Engineering
  • Serve as the primary technical owner for the configuration, administration, and enhancement of the Alation data catalog.
  • Implement and maintain automated metadata ingestion, lineage, stewardship, and documentation workflows.
  • Ensure metadata standards are consistently applied to support data discovery, trust, and reuse.
  • Partner with data governance, data quality, and architecture counterparts to embed policies, standards, and controls into catalog-driven workflows.
• Data Management & Automated Ingestion
  • Develop automation that captures technical and business metadata as data is ingested into the Enterprise Data Lake.
  • Integrate metadata, ownership, and quality context into ingestion pipelines to reduce manual effort and rework.
  • Collaborate with data engineers and platform counterparts to align ingestion patterns with data management requirements.
  • Help ensure data management considerations are addressed early in the data lifecycle.
  /li
• Agentic & Intelligent Data Enablement
  • Design and implement agentic or AI-assisted solutions that enhance data management processes, including:
  • Metadata enrichment and classification
  • Data quality rule creation and monitoring
  • Governance policy support and enforcement
  • Support the evolution from manual, reactive processes to scalable, automated data management capabilities.
• Cross-Discipline Collaboration
  • Work closely across governance, quality, architecture, and platform disciplines to deliver cohesive data management solutions.
  • Translate data management standards and requirements into concrete technical implementations.
  • Contribute documentation, reusable patterns, and best practices that improve consistency and shared ways of working.

• Experience in data engineering, analytics engineering, or data platform-focused roles.
• Hands-on experience with cloud-based data platforms, preferably AWS (e.g., S3, Glue, Athena, Redshift, Lake Formation, or similar).
• Experience working with data catalogs or metadata management tools (Alation preferred).
• Proficiency with SQL and at least one programming language (e.g., Python).
• Working knowledge of data management concepts including metadata, lineage, data quality, and access controls.
• Ability to collaborate effectively within a cross-disciplinary data management organization.

• Exposure to data quality frameworks or AI-driven data quality tooling.
• Experience working in data governance or data product-oriented operating models.
• Familiarity with automated data ingestion pipelines and metadata-driven architecture.
• Experience in a federated or hybrid data governance model.
• Experience in Agile environments or…