Sr. Performance & Insights Engineer

The Performance & Insights Engineer sits within SM Energy's Enterprise Performance & Insights team, reporting into the Chief Operating Officer organization, and works at the intersection of petroleum engineering, data engineering, and executive decision support. This role translates complex subsurface, operational, and financial data into high-impact analyses, models, and executive-ready deliverables that inform Investor Relations (IR) and Board of Directors (BOD) communications and advance critical strategic initiatives. Combining a working command of operational and asset engineering fundamentals with full-stack data wrangling and coding capability and modern AI-assisted workflows, the engineer independently frames ambiguous business problems, builds scalable end-to-end analytical solutions, and communicates insights persuasively to senior leadership. Operates under limited review while handling assignments of broad scope and complex difficulty; may mentor less experienced analysts and engineers and supervise their technical work.

Essential Roles and Responsibilities

• Translate complex operational, development, and financial business problems into end-to-end analytical solutions — from data acquisition and pipeline through model, visualization, and executive-ready deliverable.
• Build, maintain, and optimize data pipelines, queries, and analytics data views across production, reservoir, financial, and capital-planning domains using SQL and Python; design and test scripts, stored procedures, functions, and views.
• Deliver executive and Board-ready artifacts through an agentic, AI-assisted workflow — directing tools such as Claude to build self-contained HTML dashboards, branded decks, and models from a spec, then refining them while retaining judgment over accuracy and design.
• Understanding of production and reservoir engineering fundamentals — decline curve analysis (DCA), rate/pressure transient analysis (RTA/PTA), volumetrics, EURs, artificial lift, and production optimization — to QA/QC data and pressure-test analytical results for physical reasonableness.
• Develop capital-efficiency, production-performance, operational KPIs, and economic metrics and models that support budgeting, forecasting, and strategic decision-making across operating regions.
• Design and produce executive-facing reporting — BOD slides, IR materials, and interactive dashboards — built to SM Energy brand standards.
• Leverage AI tools (e.g., large language model assistants such as Claude) to accelerate code development, data QA/QC, documentation, and analysis, applying sound engineering judgment to validate and stress-test AI-assisted output.
• Develop correlations between geological, completion, operational, and financial variables and well- and asset-level performance.
• Perform ad-hoc analyses; troubleshoot data-quality issues across the business; proactively identify potential issues and recommend solutions.
• Serve as a liaison across Operations, Finance, IR, Reservoir/Asset teams, and IT/Data — bridging deeply technical work and business audiences.
• Monitor production, capital, and financial data for discrepancies and opportunities; recommend operational and process improvements.
• Contribute to data governance, code documentation, change-release process, and best practices; educate others in support of a data- and AI-first culture.
• Manage and engage in multiple initiatives simultaneously; independently prioritize tasks and eliminate roadblocks to meet target deadlines.
• Conduct business with the highest ethical standards; demonstrate decision-making that ensures the safety of all people associated with SM facilities.
• Other duties as assigned.

Role-Specific Technical Knowledge & Skills

Petroleum Engineering. Working command of production operations, artificial-lift applications, and fundamental reservoir engineering disciplines — decline curve analysis, rate/pressure transient analysis, volumetrics, recoveries, EURs, and recovery factors. Understanding of unconventional resource plays across multiple basins/operating regions and familiarity with reserves, economics, AFE, and capital-planning workflows. Ability to identify limitations and ranges of certainty in a data set and to relate reservoir, completion, and operational characteristics to well performance.

Data Engineering & Analytics. Advanced ability to wrangle, pre-process, and feature-engineer data and to connect data from disparate sources to surface insights, data-quality issues, and variable correlations. Sound applied-statistics foundation; competence with machine-learning concepts and frameworks a plus. End-to-end pipeline design that is cost-effective and scalable; strong code documentation and change-management discipline.

Computer/Software Applications

• Programming & Analytics: Advanced SQL and Python (pandas, numpy, and related data ecosystem); R or comparable a plus.
• Data Platforms & Architecture: Data virtualization via Denodo; Snowflake and/or Databricks; Palantir Foundry; relational database design; ETL/ELT (dbt, SSIS); cloud environments (Azure, AWS).
• Visualization & Reporting: Governed enterprise BI (Power BI, Spotfire, or Tableau) for refreshing dashboards, plus bespoke interactive tools built as self-contained HTML dashboards (D3, Plotly, Recharts) that run anywhere without a license or server. Advanced PowerPoint and Excel for executive and Board deliverables, produced to SM Energy brand standards.
• Development Tools: Git/GitHub, modern IDEs (VS Code, PyCharm), notebooks (Jupyter), and fast dependency/environment management (uv, Poetry, conda). Comfortable in the agentic developer loop — coding assistants such as Claude Code, Cursor, or GitHub Copilot working inside the IDE/terminal, not used only as a side chat.
• Agentic Delivery: Fluent in AI-assisted artifact creation — directing agentic tools (e.g., Claude) to build branded HTML dashboards, PowerPoint decks, and Excel models end to end from a specification, then refining them iteratively. Applies SM's visual standards programmatically ("brand-as-code") so output is board-ready by default, and exercises judgment over layout, data integrity, and narrative clarity rather than accepting first-pass output.
• AI-Augmented Development: Fluent in an AI-native analytics workflow — uses LLM assistants (e.g., Claude) and agentic tools to accelerate development, QA/QC, refactoring, and documentation, always validating output against engineering judgment. Can leverage the Model Context Protocol (MCP) to connect AI agents securely to enterprise data and tools (e.g., querying Denodo or Snowflake, drafting Office deliverables) and platform-native AI such as Snowflake Cortex and Power BI Copilot, keeping data security and governance front of mind. 
• Petroleum Engineering Software: Aries, Wellview, and Spotfire; exposure to RTA/PTA and production-analysis tools; reservoir simulation and frac models a plus.

Education

• Bachelor's degree required in Petroleum, Chemical, or Mechanical Engineering; Data Science; Computer Science; Statistics; or a related quantitative field. 
• A dual foundation in engineering and computing/data is strongly preferred. 
• Advanced degree a plus.

Typical Experience

• Minimum of 6 years of relevant experience. 
• Experience should span both petroleum engineering (production/operations, and/or reservoir) and hands-on data/analytics development. 
• Demonstrated delivery of executive- or Board-facing analytical work is strongly preferred.

SM Energy offers competitive compensation and benefits programs which include, but are not limited to, variable pay, health care coverage, retirement plan, protection coverage, time off and leave programs, training and development opportunities and a range of allowances connected to specific work situations. Details are available at Careers :: SM Energy Company (SM) (sm-energy.com).

Applications will be accepted on an ongoing basis until the position is filled.