Sr. Manager, Data Engineering

Helmerich & Payne•Tulsa, OK

1d•Onsite

About The Position

Helmerich and Payne is seeking a strategic Senior Manager, Data Engineering to own the strategy, delivery, and governance of enterprise data platforms. The role sets 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 make data a trusted, reusable asset for business intelligence, operational reporting, and AI and machine learning. The Senior Manager will deliver scalable, accessible data repositories and enable AI tools to consume data through MCP servers, APIs, and other methods. The position establishes trusted data models and defines the data needed to support AI initiatives, while owning the enterprise data engineering strategy and multi-year roadmap. It also leads platform architecture, investments, governance, modernization, and operational excellence. The role calls for strong experience with enterprise ERP, finance, accounting, procurement, supply chain, and oil and gas operations. Technical areas include modern cloud data platforms and architectures, Microsoft Fabric, Databricks, Azure, data pipelines, governance, DataOps, analytics, and AI data enablement. The position is based at the Tulsa Office and is onsite.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, a related field, or equivalent experience.
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.
Proven ownership of an enterprise data strategy and roadmap, and delivery of large cross-functional data initiatives.
Experience delivering data for AI and machine learning, including exposing data through APIs and modern access methods and partnering with data science or AI teams.
Strong expertise with SQL Server, Oracle, ETL/ELT development, and enterprise data modeling.
Experience with Azure, AWS, or Google Cloud data platforms and modern lakehouse architectures.
Experience supporting ERP ecosystems such as Oracle EBS, SAP, Workday, or Dynamics, and integrating ERP data into analytics platforms.
Experience owning platform budgets and vendor or managed-service-provider relationships.
Experience establishing enterprise-scale data governance, quality, and security programs.
Strong organizational leadership, including mentoring leaders and teams and setting talent and skills strategy.
Executive communication and stakeholder management skills, including the ability to influence across functions.
Strategic thinking combined with sufficient hands-on technical depth to make sound architectural decisions.
Financial acumen for platform cost and value, and disciplined prioritization across complex initiatives.
Strong functional understanding of Finance, Accounting, Procurement, and Supply Chain business processes.
Experience with enterprise ERP ecosystems, including Oracle EBS, SAP, and Workday; Oracle EBS modules include Financials, Procurement, Supply Chain, Inventory, Projects, and HR.
Experience integrating ERP data into enterprise data warehouses and analytics platforms.
Experience with oil and gas operational, drilling, production, and financial data environments, upstream business processes, and operational analytics.
Track record designing enterprise reporting and analytics for operational and financial decisions in partnership with Operations, Supply Chain, Finance, and executive leadership.
Hands-on Microsoft Fabric experience spanning Fabric Lakehouse, Warehouse, Data Factory, Real-Time Analytics, OneLake, Azure Data Lake, Power BI, ERP integration, self-service reporting, governance, security, workspace administration, and AI-enabled analytics.
Knowledge of enterprise data lake architecture, lakehouse and medallion architecture, Delta Lake and Delta Live Tables, Azure Data Lake Storage, Azure Synapse Analytics, and Microsoft Fabric.
Experience with Databricks platform administration and optimization, Apache Spark and PySpark, real-time and streaming architectures, ETL modernization, and change data capture.
Experience with MCP servers, REST and data APIs, real-time data services, data products and semantic layers for AI and application consumption, and MLflow and AI/ML data enablement.
Knowledge of data governance and master data management, Databricks SQL and Unity Catalog, Fabric governance, security and workspace administration, CI/CD pipelines, DataOps, GitHub, Git, and Azure DevOps.
Experience with analytics and BI tools such as Power BI, Tableau, Cognos, and OBIEE, and programming languages Python, SQL, and Spark.

Nice To Haves

Advanced degree preferred.
Preferred industry experience in Oil and Gas, Manufacturing, Energy, Financial Services, Healthcare, or Retail and Supply Chain.

Responsibilities

Deliver a scalable and accessible data repository to power AI models, tools, and products.
Expose data to AI through Model Context Protocol servers, APIs, and other methods, enabling timely native consumption by AI tools.
Establish data models as the source of truth in alignment with Digital Operations and Digital Drilling Solutions.
Define the type and age of data required for model development and ensure AI initiatives have needed data when required.
Own and execute the enterprise data engineering strategy and multi-year roadmap, aligned with business, functional-unit, and IT priorities.
Lead and develop a multi-team organization of data engineering managers, leads, ETL and BI developers, and data architects across locations; build organizational skills, bench strength, and succession.
Set architecture direction and standards for enterprise data lake, lakehouse, data warehouse, and cloud analytics platforms, and govern adoption.
Own data platform investments, including budget, capacity, and vendor and managed-service-provider relationships, partnering with Supply Chain on negotiations.
Establish and operate enterprise data governance, quality, security, and master data management programs.
Deliver trusted business intelligence, KPI dashboards, and self-service reporting on the enterprise data foundation.
Lead modernization and migration from legacy platforms to modern cloud ecosystems and integrate ERP data into analytics platforms.
Partner with executive and business stakeholders across Finance, Operations, Supply Chain, and IT to prioritize demand, set service levels, and measure value delivered.
Ensure platform reliability, performance, cost efficiency, and disciplined DataOps and CI/CD practices.
Support enterprise reporting and analytics for operational and financial decision-making across business functions.
Administer and optimize data platforms and analytics capabilities, including Microsoft Fabric and Databricks.