Data Architect - Data Lake & Data Engineering

Chevron • Houston, TX

2d

About The Position

Chevron is accepting online applications for the position Data Architect - Data Lake & Data Engineering through April 3, 2026 at 11:59 p.m. (Central Time). Enable business opportunities through data availability and accessibility. Perform and/or coordinate end-to-end data lifecycle management activities from source to analytics, including movement, storage, modeling, enhancement, integration, quality, and security of data throughout the enterprise. Focus on data reusability, business outcome and cost efficiency. Senior individual contributor architect accountable for hands on design, enablement, and production ownership of Chevron’s Enterprise Lakehouse architecture, implementing best practices in data engineering for AI-ready data, and architecture of enterprise AI platform. This role defines and operationalizes governed, scalable, and cost-efficient Lakehouse patterns primarily on Azure Databricks with Unity Catalog, while selectively evaluating and guiding usage of Microsoft Fabric for specific workloads. It aligns data engineering practices with the Lakehouse pattern and help architect the data AI stack using Databricks, Fabric and other tools. This is a deeply technical architecture role balancing standards and strategy with direct implementation, validation, and enablement across data engineering teams. The role has no people management responsibilities; however, the role is responsible to mentor and guide other data/solution architects in the team.

Requirements

• Azure Databricks (primary data engineering platform)
• Unity Catalog
• Delta Lake/Delta Tables
• Apache Spark (PySpark/Spark SQL)
• Microsoft Fabric/OneLake (architecture evaluation and selective usage)
• Azure Storage
• Azure DevOps (work item tracking; CI/CD concepts)

Responsibilities

• Enterprise Lakehouse reference architectures and standards
• Production validated Unity Catalog patterns and guidance
• Documented evaluations and recommendations for Microsoft Fabric and Azure Databricks
• Reusable architecture artifacts that enable consistent, governed delivery
• Define, evolve, and own the Enterprise Lakehouse architecture, with Azure Databricks as the primary data engineering platform and Microsoft Fabric evaluated for targeted workloads.
• Maintain hands-on ownership of architectural standards ensuring they are practical, enforceable, and proven at scale.
• Consult on design of scalable Lakehouse patterns supporting analytics, AI/ML, and application consumption.
• Lead adoption and operationalization of Unity Catalog, including:
• Catalog, schema, and storage location design
• Identity, access boundaries, and privilege models
• Data sharing, lineage, and governance alignment
• Define and validate Delta Lake/Delta table standards for performance, interoperability, and long-term maintainability.
• Provide hands-on guidance, examples, and enablement to data engineering teams using Spark, Delta, and Databricks SQL.
• Perform targeted architecture evaluations of Microsoft Fabric capabilities, including:
• OneLake architecture and domain organization patterns
• Consult on shortcut vs. mirroring approaches (tradeoffs, limitations, and governance impact)
• Security and governance alignment with Unity Catalog (duplication risks, access boundaries)
• Capacity planning and workload placement (Fabric capacities vs. Azure Databricks workloads)
• Interoperability patterns between Azure Databricks and Fabric
• Contribute to readiness assessments for Fabric features (e.g., Lakehouse, OneLake, RealTime Intelligence), with clear recommendations on appropriate use.
• Establish and enforce enterprise architecture standards aligned with security, compliance, and data governance policies.
• Influence CI/CD patterns for Lakehouse assets and architecture artifacts in Azure DevOps.
• Partner with platform and governance teams to ensure consistent data quality, lineage, observability, and cost optimization guardrails.
• Track and communicate architectural risks, priorities, and blockers impacting Enterprise Lakehouse adoption.
• Optimize cost efficiency by influencing:
• Workload placement decisions
• Storage and compute patterns
• Consumption and access models
• Validate performance and scalability of architecture through hands-on testing and tuning.
• Develop and maintain clear, consumable architecture artifacts, including logical and physical models, data flow diagrams, and reference patterns.
• Mentor data engineers and peer architects on Lakehouse standards, best practices, and platform usage.
• Collaborate closely with Product Management, Data Engineering, Data Lake, and Unified Data Enablement teams.
• Engage with vendors and internal stakeholders on architecture, governance, and platform direction.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed