Data Analytics Engineer

Blackbeard Operating, LLC is seeking an Analytics Engineer to serve as the connective tissue between raw operational data and the business decisions that depend on it. This role is responsible for building the analytical data models, reporting layer, and self-serve tooling that allow Reservoir Engineers, Finance, Land, and Operations to do their best work., Analytical Product Development

• Scope projects with stakeholders before writing code: define the business question, required data sources, success criteria, and expected output format so that work begins with a shared understanding of what done looks like.
• Maintain a prioritized backlog of analytical requests, weighing urgency, business impact, and technical feasibility to focus effort where it creates the most value.
• Define and encode key business metrics in reusable, version-controlled SQL models, covering production performance, decline tracking, LOE and CAPEX, budget vs. actual, well-level benchmarking, and A&D evaluation support.
• Work with Data Engineering to ensure source data arrives reliably; maintain and update models 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 of new tools by demonstrating value, gathering feedback, and iterating quickly on what users actually need.
• Support deployed applications over time: triage bug reports, incorporate user feedback, and add new features as the business evolves.

Workflow Design & Quality

• Design analytical workflows with error reduction as a first principle: build in validation checks, automated assertions, and guardrails that catch problems before results reach stakeholders.
• Implement testing at every layer: unit tests on transformation logic, row-count and freshness checks on data assets, and regression tests to catch unintended changes when models are updated.
• Identify and eliminate manual, error-prone steps in recurring workflows, replacing them with reproducible, version-controlled code that runs reliably without hand-holding.
• Document assumptions, known edge cases, and data quirks so that analytical outputs can be trusted and reproduced by anyone on the team.
• Cross-check outputs against independent sources before shipping. The right candidate has a sharp instinct for catching plausible-looking wrong numbers and builds verification into their workflow by default.

Cross-Functional Collaboration

• Partner with Reservoir Engineering, Finance, Land, and Operations to translate business needs into analytical solutions.
• Serve as the internal expert on what the data means: understanding provenance, known quirks, and the right way to interpret key metrics.
• Challenge how data is currently recorded and stored across the organization. Many source datasets live in spreadsheets or ad hoc structures that introduce error and limit analytical reach. Partner with teams to redesign these workflows at the source rather than working around them downstream.

Communication & Decision Support

• Present findings and recommendations to management in a clear, concise, and actionable way, not just delivering outputs but framing what they mean and what should happen next.
• Proactively surface insights from the data rather than waiting to be asked: flag anomalies, trends, and risks as they emerge so that leadership can act on them in time to matter.
• Translate complex analytical outputs into plain language for non-technical audiences, tailoring the level of detail to the decision being made.
• Balance depth of analysis with timeliness, knowing when a thorough answer is worth the wait and when a well-reasoned quick read is more valuable., * Business 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, and everyone is working from the same numbers.
• Reservoir engineers, finance, and operations can self-serve on routine data questions and rely on Streamlit applications that are accurate and current.
• Data models are well-documented and understandable to someone picking them up for the first time.
• The engineer is viewed as a trusted translator between technical data systems and business decision-making.
• Reporting quality improves measurably over time: fewer one-off requests, fewer data discrepancies, higher stakeholder confidence.

This is a senior individual contributor role. The ideal candidate combines strong SQL and data modeling skills with genuine curiosity about the oil and gas business, and has the judgment to know when to build a Streamlit app versus when to hand a power user a well-structured dataset. You will work directly with engineers and business stakeholders to define what good data looks like, build it, and make it accessible.

Blackbeard’s analytical environment spans SQL Server, Snowflake, and Python. We’re looking for someone who can bring rigor and structure to how we model and present operational data, and who takes personal pride in the accuracy and usability of the outputs they ship., * 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; hands-on experience with pandas, numpy, plotly, matplotlib, and Streamlit required. Plotly and matplotlib are both used - charts are frequently built twice, once as interactive Streamlit components and once as matplotlib PNGs for export into decks and emails.
• Git proficiency: branching strategies, pull requests, and code review workflows; experience with pytest and golden-master or regression testing strongly preferred.
• Actively uses Claude and AI coding tools to accelerate development: not just aware of them, but fluent in applying them to real analytical problems.
• Expert-level SQL across T-SQL and Snowflake, working across multiple unrelated schemas. Must be able to diagnose why a join silently drops rows and reason carefully about aggregation grain.
• Deep familiarity with upstream oil and gas concepts required: 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 dramatically shortens ramp-up.
• Experience generating deliverables programmatically (python-pptx, XlsxWriter, or equivalent). Deliverable generation is a larger share of this role than most JDs imply.
• Geospatial experience (geopandas, shapely, pyproj) preferred.
• Ability to translate ambiguous business questions into precise data definitions and well-structured models.
• Strong attention to metric consistency and data integrity: you don’t publish numbers you haven’t verified.
• Comfort extending a large, actively evolving codebase rather than building from scratch.
• Effective communicator who can work fluently with both engineers and non-technical business stakeholders.
• Comfort operating in a fast-moving, lean environment where priorities shift and pragmatism matters.

About Blackbeard Operating

Blackbeard Operating is a Fort Worth-based, NGP-backed E&P company founded in 2014 with operations exclusively in the Permian Basin. We are the most active operator on the Central Basin Platform (CBP), with a primary focus on the southern CBP across Crane and Winkler Counties. Since inception, we have drilled over 1,000 wells across 12+ distinct reservoir targets - spanning shallow carbonates to deep unconventional plays - and currently produce approximately 70,000 gross BOEPD. We are running four rigs today with significant runway ahead, and we see a substantial opportunity set for continued organic growth, bolt-on acquisitions, and strategic partnerships that we are actively working to capture.

We take culture seriously at Blackbeard. We operate with a servant leadership mentality where individual contributors have real influence, decisions get made quickly, and the work you do is visible to leadership. Our team moves fast, expects ownership, and has no patience for bureaucracy. If you want to build something that matters and see its direct impact on the business, this is the right place.