Data Analytics Engineer

Blackbeard Operating, LLC; Fort Worth (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

Blackbeard Operating, LLC is seeking a senior Analytics Engineer to connect operational data to business decisions in an onsite role in Fort Worth, TX. You will build analytical data models, a reporting layer, and self-serve tools so stakeholders can obtain accurate answers without submitting tickets for every request.

You will work with engineering and business partners to translate questions into precise data definitions and reusable metrics, delivering Streamlit apps with documentation to

Qualifications

• Bachelor's degree in Engineering, Mathematics, Computer Science, Finance, or a related field.
• 5-10 years of experience in analytics engineering, BI, or data analytics.
• Strong Python proficiency with pandas, numpy, plotly, matplotlib, and Streamlit.
• Git proficiency including branching strategies, PRs, and code review workflows; pytest and golden-master or regression testing strongly preferred.
• Fluency in using Claude and AI coding tools to accelerate real analytical development work.
• Expert-level SQL across T-SQL and Snowflake, including diagnosing why joins can silently drop rows and reasoning carefully about aggregation grain.
• Upstream oil and gas concepts such as Arps decline, type curves, EUR, per-lateral-foot normalization, IRR, PV10, WI/NRI, LOS, AFE variance. Petroleum exposure helpful but not required.
• Experience generating deliverables programmatically using python-pptx, XlsxWriter, or equivalent; deliverable generation important.
• Geospatial experience with geopandas, shapely, and pyproj is preferred.

Responsibilities

• Scope analytics projects with stakeholders, defining business questions, data sources, success criteria, and outputs.
• Maintain a prioritized backlog of analytics requests based on urgency and impact.
• Define and encode key metrics in reusable SQL models across production performance, budget vs. actual, benchmarking, and more.
• Collaborate with Data Engineering to ensure reliable data delivery and update models as sources change.
• Deploy and support stakeholder-facing Streamlit applications with documentation and onboarding.
• Drive adoption by gathering feedback and iterating on user needs.
• Triage bug reports, incorporate feedback, and add features as the business evolves.
• Design analytics workflows with validation checks and automated assertions.

Skills

Python

SQL

Git

Geospatial

Streamlit

Pandas/Numpy

Data modeling

Claude AI tools

Testing/QA

Education

Bachelor's degree

Tools

pytest

python-pptx

XlsxWriter

geopandas

shapely

pyproj

Snowflake

Streamlit

Job description

Blackbeard Operating, LLC is looking for a senior Analytics Engineer to connect operational data to business decisions. In this onsite role in Fort Worth, TX, you will build analytical data models, a reporting layer, and self-serve tools so stakeholders can get accurate, consistent answers without creating new tickets for every request.

You will work closely with engineering and business partners to translate real-world questions into clear data definitions and reusable metrics. Deliverables are made to be used, including Streamlit applications with documentation that supports independent stakeholder operation. The role also emphasizes reducing errors through validation, testing, and guardrails, while continuously improving reporting quality over time.

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 models for topics including production performance, decline tracking, LOE and CAPEX, budget vs. actual, well-level benchmarking, and support for A&D evaluation.
• Partner with Data Engineering to ensure reliable upstream data delivery and update models as definitions and data sources change.
• Deploy and support stakeholder-facing tools as Streamlit 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 with Reservoir Engineering, Finance, Land, and Operations to 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 bring

• 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.
• Fluency in using Claude and AI coding tools to accelerate real analytical development work.
• Expert-level SQL across 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 experience with 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 like

• Stakeholders 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