Blackbeard Operating, LLC is seeking a hands-on Data Engineer to design and maintain the data infrastructure that supports operational and financial decision-making. The position focuses on turning raw data from production systems and third-party providers into dependable, analytics-ready pipelines and datasets.
Responsibilities
- 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 external data sources such as IHS/Enverus, ComboCurve, land systems, and AFE/cost tracking platforms; implement and maintain API or file-based ingestion processes as required.
- Ensure critical business fields and measures propagate correctly across systems, including production volumes, well headers, ownership data, and economics.
- Own pipeline failures end-to-end: diagnose issues, apply fixes, and prevent recurrence.
- Automate data workflows currently handled in Excel or via ad hoc scripts.
- Manage and evolve Blackbeard’s core SQL Server environment, including schema design, indexing, and performance optimization.
- Make pragmatic architectural decisions for data storage, transformation patterns, and tooling without overengineering.
- Evaluate and integrate new tools and data sources as the organization’s needs change.
- Implement testing across the pipeline lifecycle: validate inputs at ingestion, assert expected row counts and value ranges mid-transform, and run regression checks to detect unintended changes.
- Build monitoring and alerting systems to detect data quality issues before data reaches business users.
- Define and enforce data standards, naming conventions, and documentation practices.
- Maintain documentation covering pipeline architecture, data lineage, and known data quirks to keep the system understandable for current and future contributors.
- Partner with source system owners to understand data provenance and resolve issues at their root cause.
- Collaborate with Reservoir Engineering, Finance, Land, and Operations to translate data needs into engineering deliverables.
- Act as the technical point of contact for questions related to data access, availability, and reliability.
- Support Analytics Engineering by delivering clean, well-documented data assets.
- Challenge current approaches to how data is recorded and stored; when datasets arrive as spreadsheets or ad hoc files, drive adoption of structured, queryable formats at the source to reduce downstream cleanup and improve scalability.
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, with strong hands-on experience with SQL Server.
- Proficiency in Python for data transformation, automation, and pipeline development.
- Demonstrated experience designing and maintaining production-grade ETL/ELT pipelines.
- Experience with orchestration tools such as Airflow or Azure Data Factory; familiarity with dbt and Snowflake is a plus.
- Familiarity with upstream oil and gas data, including production volumes, well headers, decline curves, and AFE/cost data, is strongly preferred.
- Experience integrating with O&G data providers such as IHS, Enverus, or ComboCurve is 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, with a commitment to only shipping pipelines that can be trusted.
- Strong Python proficiency with hands-on experience using pandas/polars, SQLAlchemy/pyodbc, requests, pydantic, pytest, and Streamlit.
- Git proficiency, including branching strategies, pull requests, and code review workflows.
- Actively uses Claude and AI coding tools to accelerate development and apply them to real engineering problems.
- Effective communicator who can translate technical constraints into plain language for non-technical stakeholders.
Technologies
SQL, SQL Server, Python, ETL/ELT, SCADA, ComboCurve, IHS/Enverus, Airflow, Azure Data Factory, dbt, Snowflake, pandas, polars, SQLAlchemy, pyodbc, requests, pydantic, pytest, Streamlit, Git, Claude
Location and Experience
- Location: Fort Worth, TX (onsite)
- Minimum experience: 5 years
What Success Looks Like
- Reliable production, operational, and financial data flows from source systems to analytics-ready datasets without manual intervention.
- Data quality problems are identified through monitoring before they reach business users.
- Reservoir engineering, finance, and operations teams trust the data they receive and can act on it with confidence.
- Data infrastructure scales alongside the company without accumulating technical debt.
- Business users spend less time wrangling data and more time deriving insights.
- The engineer is viewed as a trusted partner by technical and operational teams.
