Data Architect

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HF Sinclair's Data Architect designs and governs secure, scalable, trusted, and reusable enterprise data architecture across the oil and gas value chain. The role translates business and technical needs into target-state and transition architectures spanning source systems, integration, data engineering, cloud platforms, governance, analytics, data products, and AI/ML, while partnering with business, engineering, application, security, infrastructure, governance, analytics, and enterprise architecture teams.

The role also architects governed data foundations and reusable services for data science, machine learning, generative AI, and enterprise AI agents.

• Define enterprise data principles, standards, reference architectures, roadmaps, reusable patterns, and architecture decision guidance; create conceptual, logical, physical, dimensional, relational, canonical, master-data, and semantic models.
• Architect data warehouses, lakes, lake houses, curated layers, data products, and semantic models that support reporting, Power BI, governed self-service analytics, Streamlit or similar data applications, AI agents, intelligent applications, AI/ML, and advanced analytics.
• Define architecture standards for data science, AI/ML, feature engineering, model deployment, monitoring, retraining, and MLOps/LLMOps.
• Architect secure AI-agent and generative AI solutions using foundation models, RAG, embeddings, vector search, orchestration, APIs, tools, and human oversight.
• Establish reusable AI data services, including governed ingestion, indexing, semantic retrieval, evaluation datasets, and source-to-response traceability.
• Partner with data science, ML engineering, application, security, risk, and business teams to operationalize AI solutions with measurable value and governance.
• Define responsible AI controls covering privacy, security, safety, evaluation, hallucination testing, explainability, auditability, and production monitoring.
• Design end-to-end ingestion, ETL/ELT, replication, transformation, orchestration, and delivery of raw, curated, and analytical data from SAP and other enterprise systems to Snowflake, SAP BW, cloud, and analytics platforms; guide performance, observability, reconciliation, restart, recovery, and maintainable pipeline design.
• Establish enterprise integration-backbone standards for API-led, event-driven, application-to-application, B2B, batch, near-real-time, and governed file-based exchange with internal systems, vendors, clients, partners, and financial institutions; define canonical models, schema evolution, encryption, identity, monitoring, auditability, retention, error handling, and service levels.
• Govern data ownership, stewardship, critical data elements, metadata, lineage, quality, classification, access, privacy, reference data, master data, and MDM operating-model requirements across refining, commercial, logistics, supply chain, trading, finance, and operations.
• Embed scalability, reliability, resiliency, maintainability, security, role-based access, segregation of duties, SOX, audit evidence, compliance, and data-retention controls into architecture and delivery patterns.
• Assess platform interoperability and recommend fit-for-purpose capabilities across Snowflake, Azure, Microsoft Fabric, Power BI, SAP BW, SAP Business Objects, SAP BTP Integration Suite, Azure Data Factory, Qlik Replicate and Compose, Cognite, Streamlit, Informatica, Alteryx, Collibra, managed file transfer, RPA, data catalog, MDM, and comparable technologies.
• Provide architecture oversight for modernization, cloud and database migration, reporting rationalization, integration modernization, archival, legacy coexistence, technical-debt reduction, and data-product delivery; document data flows, solution options, standards, sequencing, and executive recommendations.
• Align data products and analytics with strategic insight, operational decisions, accounting accuracy, reconciliation, process automation, business agility, adoption, and measurable outcomes; serve as a pragmatic technical advisor across delivery teams and senior stakeholders.

• Define enterprise data principles, standards, reference architectures, roadmaps, reusable patterns, and architecture decision guidance; create conceptual, logical, physical, dimensional, relational, canonical, master-data, and semantic models.
• Architect data warehouses, lakes, lake houses, curated layers, data products, and semantic models that support reporting, Power BI, governed self-service analytics, Streamlit or similar data applications, AI agents, intelligent applications, AI/ML, and advanced analytics.
• Define architecture standards for data science, AI/ML, feature engineering, model deployment, monitoring, retraining, and MLOps/LLMOps.
• Architect secure AI-agent and generative AI solutions using foundation models, RAG, embeddings, vector search, orchestration, APIs, tools, and human oversight.
• Establish reusable AI data services, including…