Description
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.
Summary
Blackbeard Operating, LLC is seeking a Data Engineer to build and maintain the data infrastructure that powers operational and financial decision-making across the company. This role sits at the intersection of engineering and the field, translating raw data from production systems, well operations, and third-party providers into clean, reliable pipelines that the business depends on.
This is a hands-on engineering role. The ideal candidate takes personal ownership of the systems they build, cares deeply about data quality, and has the drive to understand the upstream oil and gas context behind every dataset they touch. You will work directly with reservoir engineers, land professionals, finance, and operations to understand how data is generated and how it needs to be delivered.
Blackbeard's data environment spans SQL Server, SCADA systems, ComboCurve, and multiple third-party data providers. The right candidate will bring structure to a data stack that is growing fast alongside the company and will have the judgment to make pragmatic architectural decisions without over-engineering.
Key Responsibilities
Data Pipeline Development & Maintenance
- Design, build, and maintain ETL/ELT pipelines that move data from source systems (including SCADA, ComboCurve, SQL Server, and third-party providers) into centralized, analytics-ready datasets.
- Integrate data from external providers including IHS/Enverus, ComboCurve, land systems, and AFE/cost tracking platforms; build and maintain API or file-based ingestion processes as needed.
- Ensure production volumes, well headers, ownership data, and economics flow correctly across systems.
- Own pipeline failures end-to-end: diagnose, fix, and prevent recurrence.
- Automate manual data workflows currently handled in Excel or ad hoc scripts.
Data Infrastructure & Architecture
- Manage and evolve Blackbeard's core SQL Server environment, including schema design, indexing, and performance optimization.
- Make sound architectural decisions on data storage, transformation patterns, and tooling without overengineering.
- Evaluate and integrate new tools and data sources as the company's needs evolve.
Quality, Governance & Documentation
- Implement testing at every layer of the pipeline: validate inputs at ingestion, assert expected row counts and value ranges mid-transform, and run regression checks to catch unintended changes when pipelines are updated.
- Build monitoring and alerting to catch data quality issues before they reach business users.
- Define and enforce data standards, naming conventions, and documentation practices.
- Maintain documentation of pipeline architecture, data lineage, and known data quirks so the system is understandable to anyone who needs to work with it.
- Partner with source system owners to understand data provenance and resolve issues at the root.
Cross-Functional Collaboration
- Work closely with Reservoir Engineering, Finance, Land, and Operations to understand data needs and translate them into engineering solutions.
- Serve as the technical point of contact for data access, availability, and reliability questions.
- Support Analytics Engineering by delivering clean, well-documented data assets.
- Challenge how data is currently recorded and stored across the organization. Many source datasets arrive as spreadsheets or ad hoc files that introduce error and limit scalability. Push teams to capture data in structured, queryable formats at the source rather than cleaning up problems downstream.
Requirements
- Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field.
- 5–10 years of experience in data engineering or a closely related role.
- Expert-level SQL; strong hands-on experience with SQL Server specifically.
- Proficiency in Python for data transformation, automation, and pipeline development.
- Demonstrated experience designing and maintaining production-grade ETL/ELT pipelines.
- Experience with pipeline orchestration tools such as Airflow or Azure Data Factory. Familiarity with dbt and Snowflake a plus.
- Familiarity with upstream oil and gas data (production volumes, well headers, decline curves, AFE/cost data) strongly preferred.
- Experience integrating with O&G data providers such as IHS, Enverus, or ComboCurve a plus.
- Comfort working in a lean, fast-moving environment without a large data platform team behind you.
- Strong attention to data quality and documentation: you don't ship pipelines you wouldn't trust yourself.
- Strong Python proficiency; hands-on experience with pandas/polars, SQLAlchemy/pyodbc, requests, pydantic, pytest, and Streamlit.
- Git proficiency: branching strategies, pull requests, and code review workflows.
- Actively uses Claude and AI coding tools to accelerate development: not just aware of them, but fluent in applying them to real engineering problems.
- Effective communicator who can translate technical constraints into plain language for non-technical stakeholders.
What Success Looks Like
- Production, operational, and financial data flows reliably from source systems to analytics-ready datasets, without manual intervention.
- Data quality issues are caught by monitoring before they reach the business.
- Reservoir engineers, finance, and operations teams trust the data they receive and can act on it confidently.
- The data infrastructure scales alongside the company without accumulating technical debt.
- Business users spend less time wrangling data and more time deriving insight from it.
- The engineer is viewed as a trusted partner by the technical and operational teams they support.