Data Analytics Engineer

What you'll do: Scope analytics projects with stakeholders first, including the business question, required data sources, success criteria, and the expected output format. Maintain a prioritized backlog of analytical requests using urgency, business impact, and technical feasibility to focus effort on what creates the most value. Define and encode key business metrics in reusable, version-controlled SQL modelsfor topics including production performance, decline tracking, LOE and CAPEX, budget vs. actual, well-level benchmarking, and support for A&D evaluation. Partner withData Engineeringto ensure reliable upstream data delivery and update models as definitions and data sources change. Deploy and support stakeholder-facing tools asStreamlit applications, including documentation and lightweight onboarding. Drive adoption by demonstrating value, collecting feedback, and iterating quickly on what users actually need. Support deployed applications over time by triaging bug reports, incorporating feedback, and adding features as the business evolves. Design analytical workflows with validation checks, automated assertions, and guardrails that catch issues before outputs reach stakeholders. Implement testing across layers, including unit tests for transformation logic, and row-count and freshness checks for data assets, plus regression tests to detect unintended changes. Eliminate manual, error-prone steps by replacing them with reproducible, version-controlled code. Document assumptions, known edge cases, and data quirks to support trusted and reproducible outputs. Cross-check outputs against independent sources before shipping. Collaborate withReservoir Engineering, Finance, Land, and Operationsto translate business needs into analytical solutions. Serve as an internal expert on what the data means, including provenance and correct interpretation of key metrics. Challenge how data is recorded and stored, especially when sources rely on spreadsheets or ad hoc structures, and help redesign workflows at the source. Present findings and recommendations to management clearly and actionably, framing what the results mean and what should happen next. Proactively surface insights by flagging anomalies, trends, and risks as they emerge. Translate complex outputs into plain language for non-technical audiences, tailored to the decision being made. Balance depth with timeliness, delivering the right level of detail for each decision. What you'll bringBachelor's degreein Engineering, Mathematics, Computer Science, Finance, or a related field. 5-10 yearsof experience in analytics engineering, business intelligence, or data analytics. Strong Python proficiencywith hands-on experience in pandas, numpy, plotly, matplotlib, and Streamlit. Git proficiency, including branching strategies, pull requests, and code review workflows; experience with pytest and golden-master or regression testing strongly preferred. Fluency in usingClaude and AI coding toolsto accelerate real analytical development work. Expert-level SQLacross T-SQL and Snowflake, including diagnosing why joins can silently drop rows and reasoning carefully about aggregation grain. Deep familiarity with upstream oil and gas concepts such as Arps decline, type curves, EUR, per-lateral-foot normalization, IRR, PV10, WI/NRI, LOS, AFE variance, price decks, and well identity (API-10/PROPNUM/UWI). A petroleum engineering degree is not required, but meaningful E&P exposure helps ramp-up. Experience generating deliverables programmatically using python-pptx, XlsxWriter, or equivalent, with deliverable generation forming a larger share of the role than typical analytics engineering positions. Geospatial experiencewith geopandas, shapely, and pyproj is preferred. Ability to translate ambiguous business questions into precise data definitions and well-structured models. Strong attention to metric consistency and data integrity, including verifying numbers before publishing. Comfort extending a large, actively evolving codebase rather than building from scratch. Effective communication with both engineers and non-technical stakeholders. Comfort working in a fast-moving, lean environment where priorities shift and pragmatism is required. Success looks likeStakeholders have fast, reliable access to the data they need without filing a ticket or waiting for a custom pull. Key metrics are defined consistently across teams, with everyone working from the same numbers. Reservoir engineers, finance, and operations can self-serve on routine questions using accurate and current Streamlit applications. Data models are well-documented and understandable for new teammates. You are viewed as a trusted translator between technical data systems and business decision-making. Reporting quality improves over time through fewer one-off requests, fewer data discrepancies, and higher stakeholder confidence.

Technologies: SQL, T-SQL, Snowflake, Python, pandas, numpy, plotly, matplotlib, Streamlit, Git, pytest, Claude, python-pptx, XlsxWriter, geopandas, shapely, pyproj