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. 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.