Data Architect

HF Sinclair•Dallas, TX

4d•Onsite

About The Position

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.

Requirements

A minimum of 10 years of experience required.
Five years of job related SAP work experience and five years of non-sap architecture experience is required.
Experience in data architecture, data engineering, data modeling, enterprise analytics, integration, cloud data platforms, or related technology roles, including cross-functional delivery with business, application, engineering, analytics, governance, security, infrastructure, and enterprise architecture teams.
Strong knowledge of conceptual, logical, physical, dimensional, relational, canonical, master-data, and modern analytical modeling; data warehouses, lakes, lakehouses, cloud platforms, curated layers, BI, and semantic models.
Working knowledge of data science and AI/ML concepts, including statistical analysis, feature engineering, model evaluation, deployment, monitoring, and MLOps practices.
Understanding of generative AI and agent architecture, including foundation models, RAG, embeddings, vector databases, prompt engineering, orchestration, guardrails, and evaluation.
Strong understanding of API-led, event-driven, application, B2B, managed file transfer, batch, and near-real-time integration, plus ETL/ELT, replication, orchestration, observability, reconciliation, performance, restart, and recovery patterns.
Strong knowledge of data governance, metadata, lineage, quality, MDM, classification, privacy, role-based access, SOX, auditability, retention, control evidence, and stewardship operating models.
Strong communication, collaboration, and stakeholder-management skills, with the ability to work effectively across cross-functional technology teams and business stakeholders, build alignment, facilitate decisions, and clearly communicate complex architecture concepts to technical and non-technical audiences
Knowledge of data product operating models, including domain ownership, product lifecycle management, data contracts, discoverability, quality SLAs, metadata, reuse, value realization, and self-service consumption patterns across analytics, AI, and business capabilities.
Ability to translate requirements into scalable designs, evaluate trade-offs, manage technical complexity, influence architecture decisions, and communicate clearly through architecture artifacts and executive-ready recommendations.
Experience defining and governing data products that align business outcomes, architecture standards, governance requirements, and platform capabilities.
Strong documentation skills with the ability to translate complex technical architectures into clear, business-friendly deliverables.
Proficient in Microsoft Word, Visio, PowerPoint, and related collaboration tools for creating architecture diagrams, solution designs, technical specifications, process flows, and executive presentations.
Understanding oil and gas processes, preferably refining, commercial, logistics, supply chain, trading, operations, or finance.

Nice To Haves

Experience with Snowflake, Databricks, Azure, Microsoft Fabric, Power BI, SAP BW, SAP BusinessObjects, SAP BTP Integration Suite, Azure Data Factory, Qlik Replicate and Compose, Cognite, Streamlit, Informatica, Alteryx, Collibra, managed file transfer, RPA, or similar enterprise data, integration, analytics, and AI technologies.
Experience with Python, SQL, notebooks, ML frameworks, experiment tracking, Azure AI, Snowflake Cortex, Copilot Studio, and similar AI/ML technologies.
Experience designing AI agents, RAG solutions, intelligent applications, and governed AI products integrated with enterprise data and workflows.

Responsibilities

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, lakehouses, 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 BusinessObjects, 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.
Special assignments or tasks assigned to the employee by their superior, as determined from time to time in their sole and complete discretion.