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United States | 8+ years

Enterprise Data Architect

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Take the Data lake vision and high architecture as defined, turn it into concrete low-level designs, work packages, explain and hand them to the engineering team, and work with the project managers to deliver on phase deadlines. Hands on ~50%, deep enough to review, unblock and set the technical bar. Owns outcomes end to end, finds and fixes problems, and multiplies that reach with AI native way of working.

Responsibilities

  • Translate vision into design. Take the architectural direction and produce implementable designs, strategize and translate to tasks. Keep the vision and high-level design intact through delivery without redefining the vision or reducing the scope.
  • Build with AI every day using Claude Code, Codex or similar. Write the markdown specs, skills and context files that make coding effective, and review what they produce to a production standard.
  • Stay hands on with the Data Lakehouse foundation. Python, PySpark, Delta Lake and medallion design on AIDP (OCI).
  • Design data products and semantic structures. Blueprints covering scope, canonical entities, certified metrics, quality rules, lineage, access, and publication criteria.
  • Set standards for keys, naming conventions, configuration, reference data, and metadata, and keep them aligned to the business glossary.
  • Understand agentic architecture. How agents consume data and tools, callable services for AI systems, MCP style interfaces, agent traces, and observability, and facilitate data access for agents.
  • Lead and delegate. Break work down, assign it, review it and coach onshore and offshore engineers. Partner with project managers on planning, estimates, and risks.
  • Know the platform and governance side. Terraform, asset bundles, CI/CD and environment promotion.
  • Turn governance policies such as PHI protection, masking, classification, and data quality into controls engineers can actually implement.
  • Oracle Golden gate CDC process, creating unified data models on Data Lake, create pipelines, conformed dimensions and Gold layer delivery to semantic models.

Required Qualifications

  • 8+ years hands-on experience in data engineering, data architecture or data lake delivery, including 2+ years leading technical teams.
  • At least 1 year experience building products using AI coding tools like Claude code or Codex.
  • Knowledge around AI frameworks for Agent observability, MCP server development or knowledge layer design (ontologies, knowledge graphs)
  • Expert in Python, PySpark, SQL, Object oriented programming with production experience on AIDP, Databricks or Snowflake.
  • Has designed conceptual, logical, physical, and dimensional models and delivered them as governed Gold assets used by BI semantic layers.
  • Uses AI coding tools daily to build production systems and can write the specs and context that make them work well.
  • Has built REST services and callable tools consumed by applications or AI agents.
  • Has delivered under regulated data controls (PHI, HIPAA or equivalent) and against fixed phase deadlines.

Preferred Qualifications or knowledge

  • Oracle Cloud Infrastructure, Golden Gate and Autonomous Database.
  • Data contracts, Data Mesh, and governed data marketplace implementation.
  • Healthcare domains: claims, provider, member, product, finance, acquisition integration.
  • Platform depth in Terraform, asset bundles, GitHub Actions and Kubernetes.
  • Experience running distributed onshore and offshore teams.