Data Analytics Engineer

Blackbeard Operating, LLC is seeking a senior Analytics Engineer in Fort Worth, TX to connect raw operational data to business decisions. In this onsite role, you will build analytics data models, create a reporting layer, and deliver self-serve tools that help engineering and business teams use the same verified metrics.

Role Summary

You will partner with engineering and business stakeholders to define data definitions and make metrics accessible through tools such as Streamlit applications. The work focuses on shared understanding of requirements upfront, reliable data foundations, and deployable models and tooling that reduce manual effort over time.

Key Responsibilities

• Scope analytics projects with stakeholders before writing code by defining the business question, required data sources, success criteria, and expected output format.
• Maintain a prioritized backlog of analytical requests, balancing urgency, business impact, and technical feasibility to focus effort on the highest-value work.
• Define and encode key business metrics in reusable, version-controlled SQL models, including production performance, decline tracking, LOE and CAPEX, budget versus actual, well-level benchmarking, and support for A&D evaluation.
• Collaborate with Data Engineering to ensure source data arrives reliably, and keep models updated as business definitions and data sources evolve.
• Deploy finished tools as Streamlit applications, with documentation and lightweight onboarding so stakeholders can operate them independently.
• Drive adoption by demonstrating value, collecting feedback, and iterating quickly based on what users actually need.
• Support deployed applications over time by triaging bug reports, incorporating feedback, and adding features as the business evolves.
• Reduce errors through analytical workflows that include validation checks, automated assertions, and guardrails that prevent problematic results from reaching stakeholders.
• Implement testing across layers, including unit tests for transformation logic, data asset row-count and freshness checks, and regression tests to detect unintended changes when models are updated.
• Eliminate manual, error-prone steps in recurring workflows by replacing them with reproducible, version-controlled code that runs reliably.
• Document assumptions, known edge cases, and data quirks to ensure outputs can be trusted and reproduced.
• Cross-check outputs against independent sources before shipping changes.
• Partner with Reservoir Engineering, Finance, Land, and Operations to translate business needs into analytical solutions.
• Act as an internal expert on what the data means, including provenance, known quirks, and appropriate interpretation of key metrics.
• Challenge how data is recorded and stored across the organization, partnering to redesign source workflows when spreadsheets or ad hoc structures introduce error or limit analytical reach.
• Present findings and recommendations to management in clear, concise, actionable language, including what the results mean and what should happen next.
• Proactively surface insights by flagging anomalies, trends, and risks as they emerge.
• Translate complex analytical outputs into plain language for non-technical audiences, tuned to the decision being made.
• Balance depth and timeliness by knowing when a thorough response is warranted versus when a well-reasoned quick read is more valuable.

Requirements

• Bachelor’s degree in Engineering, Mathematics, Computer Science, Finance, or a related field.
• 5–10 years of experience in analytics engineering, business intelligence, or data analytics.
• Strong Python proficiency with 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.
• Fluent use of Claude and AI coding tools to accelerate development on real analytical problems.
• Expert-level SQL across T-SQL and Snowflake, including the ability to diagnose silent row loss during joins and reason carefully about aggregation grain.
• Deep familiarity with upstream oil and gas concepts, including 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).
• Experience generating deliverables programmatically using python-pptx, XlsxWriter, or equivalent.
• Geospatial experience with geopandas, shapely, and pyproj preferred.
• Ability to translate ambiguous business questions into precise data definitions and well-structured models.
• Strong attention to metric consistency and data integrity, with a focus on publishing only verified numbers.
• Comfort extending a large, actively evolving codebase rather than building from scratch.
• Effective communication skills for working fluently with both engineers and non-technical stakeholders.
• Comfort operating in a fast-moving, lean environment where priorities shift.

Technologies

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

What Success Looks Like

• Business stakeholders get fast, reliable access to needed data without filing a ticket or waiting for custom pull requests.
• Key metrics are defined consistently across teams, enabling everyone to work from the same verified numbers.
• Reservoir engineering, finance, and operations can self-serve routine data questions with accurate, current Streamlit applications.
• Data models are well-documented and understandable for someone picking them up for the first time.
• The engineer is trusted as a translator between technical data systems and business decision-making.
• Reporting quality improves measurably over time through fewer one-off requests, fewer data discrepancies, and increased stakeholder confidence.

Minimum experience: 5 years. Education: Bachelor’s degree in Engineering, Mathematics, Computer Science, Finance, or a related field. Location: Fort Worth, TX (onsite).