Data Engineer

Blackbeard Operating, LLC is hiring a hands-on Data Engineer to build and maintain the data infrastructure behind operational and financial decision-making. In this onsite role in Fort Worth, TX, you will turn raw production and third-party data into clean, reliable pipelines and analytics-ready datasets, while partnering across Reservoir Engineering, Finance, Land, and Operations.

What you’ll build and improve

• 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 using API or file-based ingestion processes as needed.
• Ensure the right operational details flow correctly across systems, including production volumes, well headers, ownership data, and economics.
• Own pipeline reliability end-to-end by diagnosing failures, fixing root causes, and preventing recurrence.
• Automate workflows currently handled in Excel or ad hoc scripts to improve consistency and scalability.
• Manage and evolve the core SQL Server environment, including schema design, indexing, and performance optimization.
• Make practical architecture decisions around data storage, transformation patterns, and tooling without overengineering.
• Evaluate and integrate new tools and data sources as business needs evolve.

Quality, monitoring, and documentation

• Implement testing at every layer of the pipeline: validate inputs at ingestion, assert expected row counts and value ranges during transforms, and run regression checks to catch unintended changes.
• Build monitoring and alerting to detect data quality issues before business users are impacted.
• Define and enforce data standards, including naming conventions and documentation practices.
• Maintain documentation covering pipeline architecture, data lineage, and known data quirks.
• Partner with source system owners to understand data provenance and resolve issues at the root.

Collaboration and delivery

• 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 recorded and stored across the organization by pushing teams toward structured, queryable formats at the source instead of relying on downstream cleanup of spreadsheets and ad hoc files.

Qualifications

• 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 in 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. dbt and Snowflake familiarity 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.
• Hands-on Python skills including pandas/polars, SQLAlchemy/pyodbc, requests, pydantic, pytest, and Streamlit.
• Git proficiency (branching strategies, pull requests, code review workflows).
• Actively uses Claude and AI coding tools to accelerate development and applies them to real engineering problems.
• Effective communicator who can translate technical constraints into plain language for non-technical stakeholders.

Success looks like production, operational, and financial data flows reliably from source systems to analytics-ready datasets without manual intervention, with monitoring catching quality issues early. Teams across Reservoir Engineering, Finance, and Operations can trust the data they receive, business users spend less time wrangling data, and the infrastructure evolves without accumulating technical debt.

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