Data Engineer

Blackbeard Operating, LLC is seeking a hands-on Data Engineer to design and maintain the data infrastructure that supports operational and financial decision-making. This role converts production and third-party data into reliable, analytics-ready pipelines and datasets.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines that move data from source systems into centralized, analytics-ready datasets, including sources such as SCADA, ComboCurve, SQL Server, and third-party providers.
• Integrate external provider data, including IHS/Enverus, ComboCurve, land systems, and AFE or cost tracking platforms, using API-based or file-based ingestion as needed.
• Ensure production volumes, well headers, ownership data, and economics are correctly propagated across systems.
• Own pipeline failures end-to-end by diagnosing issues, implementing fixes, and preventing recurrence.
• Automate manual workflows currently handled in Excel or ad hoc scripts.
• Manage and evolve the core SQL Server environment, including schema design, indexing, and performance optimization.
• Make practical architectural decisions for data storage, transformation patterns, and tooling without overengineering.
• Evaluate and integrate new tools and data sources as company needs evolve.
• Implement testing across the pipeline, including ingestion input validation, mid-transform checks (such as expected row counts and value ranges), and regression testing to prevent unintended changes.
• Build monitoring and alerting to detect data quality issues before business users are impacted.
• Define and enforce data standards, naming conventions, and documentation practices.
• Maintain documentation covering pipeline architecture, data lineage, and known data quirks to support ongoing understanding and maintenance.
• Partner with source system owners to understand data provenance and resolve issues at the root.
• Collaborate with Reservoir Engineering, Finance, Land, and Operations to translate data needs into engineering solutions.
• Serve as a 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 recorded and stored across the organization, encouraging 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; minimum 5 years required.
• Expert-level SQL with strong hands-on experience using 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 (production volumes, well headers, decline curves, AFE/cost data) 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 mindset focused on building pipelines you can trust.
• Strong Python proficiency, including hands-on experience with pandas or polars, SQLAlchemy or 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, applying them to real engineering problems.
• Effective communication skills to translate technical constraints into plain language for non-technical stakeholders.

Technical Stack

• 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

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 detected by monitoring before reaching business users.
• Reservoir engineers, finance, and operations teams trust the received data and can act with confidence.
• Data infrastructure scales alongside the company without accumulating technical debt.
• Business users spend less time wrangling data and more time deriving insight.
• The engineer is viewed as a trusted partner by technical and operational teams.

Location

Fort Worth, TX (onsite)