Own the end-to-end architecture of an Azure-based data platform transitioning to Snowflake. Define lakehouse and data fabric standards for ingestion, storage, processing, governance, security, lineage, modeling, and access control across IT, OT, and IoT sources. Establish integration patterns, validate pilot use cases, review data engineering, GraphDB, and MLOps designs, and plan a phased roadmap supporting analytics and agentic AI capabilities.
Role summary
Owns the end-to-end architecture of the Data Platform, starting on Azure and moving to Snowflake. Defines the lakehouse / data fabric blueprint, validates it against the initial pilot use cases, and sets the standards that every downstream engineer builds against. This person is accountable for the platform holding up as IT, OT, and IoT sources are onboarded and as GraphDB and MLOps layers are introduced in later phases.
Key responsibilities
- Design the target data fabric / lakehouse architecture on Azure, with a clear migration path to Snowflake.
- Define ingestion, storage, processing, governance, and consumption layers, including how GraphDB fits for asset topology and ontology.
- Validate the architecture against one to two priority pilot use cases before broad rollout.
- Set data modeling, security, lineage, and access-control standards across all sources.
- Establish the reference patterns for IT / OT / IoT integration and hand them to the integration engineers.
- Review designs from data engineering, GraphDB, and MLOps workstreams for consistency with the platform blueprint.
- Plan the phased roadmap so agentic AI capabilities can be layered on once the data foundation is stable.
Must-have skills and experience
- Proven experience architecting data lakehouse or data fabric platforms end to end.
- Deep hands-on Azure data stack experience (Data Lake, Synapse / Fabric, Data Factory, or equivalent).
- Snowflake architecture and migration experience.
- Strong data modeling across relational, document, time-series, and graph paradigms.
- Experience integrating heterogeneous IT / OT / IoT sources at enterprise scale.
- Working knowledge of GraphDB / knowledge graph concepts for topology and ontology.
- Governance, lineage, and audit-trail design experience.
Nice to have
- Exposure to intelligent building management or industrial OT environments.
- Familiarity with MLOps platform requirements.
- Experience planning for agentic AI or advanced analytics on top of a data platform.
Relevant stack
Azure (Data Lake, Synapse / Fabric, Data Factory), Snowflake, GraphDB, Oracle ERP, MongoDB, PostgreSQL, Cassandra, Redis, InfluxDB, time-series stores, MinIO / NFS.
General attributes
- Proactive and self-driven, able to take ownership and move work forward without waiting to be told.
- AI-enabled in day-to-day work, comfortable using AI tools and copilots to accelerate delivery and quality.
- Strong self-learner who stays current with evolving tools, platforms, and practices.
- Good team player who collaborates well across engineering, operations, and stakeholder groups.
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