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Data Engineer

Blackbeard Operating, LLC Fort Worth, TX

Fort Worth, TX

Today

Full time Backend Developer Apache Airflow Azure

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Job Description

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)

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