Sr. Manager, Data Engineering

Location: Tulsa Office · Work Type: Onsite

About the role

We are seeking a strategic Senior Manager, Data Engineering to own the strategy, delivery, and governance of our enterprise data platforms. This leader sets the direction for data warehouse, data lake, and lakehouse architecture, leads data engineering managers and teams across multiple locations, and partners with executive and business leaders to turn data into a trusted, reusable asset that powers business intelligence, operational reporting, and AI and machine learning across the organization.

Responsibilities

• Deliver a scalable and easily accessible data repository to power AI models, tools, and products.
• Surface data to AI through Model Context Protocol (MCP) servers, APIs, and other methods so AI tools can consume it natively and in the timeframe initiatives need.
• Establish these data models as the source of truth, in close alignment with Digital Operations and Digital Drilling Solutions.
• Define the scope of data, including type and age, required to surface and effectively build models, and ensure AI initiatives have the required data when they need it.
• Own and execute the enterprise data engineering strategy and multi-year roadmap, aligned to business and functional unit and IT priorities.
• Lead and develop a multi-team organization of data engineering managers, leads, ETL and BI developers, and data architects; build bench strength, skills, and succession across locations.
• Set architecture direction and standards for enterprise data lake, lakehouse, data warehouse, and cloud analytics platforms, and govern their adoption.
• Own the data platform investment: budget, capacity, and the vendor and managed-service-provider relationships that support it, partnering with Supply Chain, who executes negotiations.
• Establish and run enterprise data governance, quality, security, and master data management as a sustained program, not a one-time project.
• Deliver capabilities for trusted business intelligence, KPI dashboards, and self-service reporting on top of the enterprise data foundation.
• Lead major modernization and migration initiatives from legacy platforms to modern cloud ecosystems, and integrate ERP data (Oracle EBS, SAP, and others) into the analytics estate.
• Partner with executive and business stakeholders across Finance, Operations, Supply Chain, and IT to prioritize demand, set service levels, and measure the value delivered.
• Ensure operational excellence across the platform: reliability, performance, cost efficiency, and DataOps and CI/CD discipline.

Domain Expertise

• Strong functional understanding of Finance, Accounting, Procurement, and Supply Chain business processes.
• Experience supporting enterprise ERP ecosystems (Oracle EBS, SAP, and Workday), including Oracle EBS modules (Financials, Procurement, Supply Chain, Inventory, Projects, and HR), and integrating ERP data into enterprise data warehouses and analytics platforms.
• Experience with oil and gas operational, drilling, production, and financial data environments, and upstream business processes and operational analytics.
• Track record designing enterprise reporting and analytics for operational and financial decision-making, partnering with Operations, Supply Chain, Finance, and executive leadership.
• Hands-on Microsoft Fabric experience: unified analytics across Fabric Lakehouse, Warehouse, Data Factory, and Real-Time Analytics; OneLake and enterprise data consolidation; integration with Azure Data Lake, Power BI, and ERP systems; self-service reporting; Fabric governance, security, and workspace administration; and AI-enabled analytics.

Technical Skills

• Data platform and architecture: enterprise data lake architecture, lakehouse design and medallion architecture, Delta Lake and Delta Live Tables, Azure Data Lake Storage (ADLS), Azure Synapse Analytics, and Microsoft Fabric (Lakehouse, Warehouse, Data Factory, Real-Time Analytics, and OneLake).
• Processing and pipelines: Databricks platform administration and optimization, Apache Spark and PySpark, real-time and streaming data architectures, ETL modernization, and change data capture (CDC).
• AI data enablement and access: Model Context Protocol (MCP) servers, REST and data APIs, real-time data services, and data products and semantic layers for AI and application consumption; MLflow and AI/ML data enablement.
• Governance and DataOps: data governance and master data management, Databricks SQL and Unity Catalog, Fabric data governance, security, and workspace administration, CI/CD pipelines and DataOps, and GitHub, Git, and Azure DevOps.
• Analytics and BI: Power BI, Tableau, Cognos, and OBIEE.
• Languages: Python, SQL, and Spark.

Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, a related field, or equivalent experience; advanced degree preferred.
• 10+ years of experience in data warehousing, data engineering, or analytics environments, including enterprise-scale platforms.
• 5+ years of leadership or management experience, including leading managers or team leads, not only individual contributors.