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Specialized Databricks/AWS expertise and seniority reduce applicant density despite metro location.
Cloud and Databricks skills are transferable, but embedded domain responsibilities increase industry specificity.
Multiple mandatory enterprise Databricks, AWS, data mesh, governance, and architecture requirements.
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Own end-to-end architecture design and delivery of AI-aligned data solutions within a specific business domain, translating enterprise architecture into practical, scalable data mesh, ML, and GenAI platforms.
Serve as the primary data architect and trusted advisor embedded in the business to enable and accelerate data, ML, and AI initiatives through hands-on leadership and stakeholder collaboration.
Represent the domain in central architectural governance forums, ensure compliance with security and data governance standards, and provide feedback to evolve enterprise data platforms and standards.
Expertise in AWS cloud data architectures and Databricks Lakehouse platforms, including Delta Lake.
Bachelor's degree in computer science, Information Technology, or related field.
Proven experience with data mesh, DataOps, MLOps, AI-aligned data patterns (feature stores, RAG pipelines), and enterprise metadata/governance tools like Unity Catalog or Glue.
Work Experience Required: Not explicitly mentioned in the JD.
Hands-on architect with deep technical expertise in designing secure, scalable, and cost-effective cloud data platforms supporting ML and Generative AI workloads.
Ability to influence and collaborate across business and technical stakeholders, balancing governance and delivery speed in complex, fast-paced environments.
Experience translating enterprise data architecture into domain-specific solutions promoting decentralized ownership and operational resilience.