Staff Data Engineer

Flywheel Energy•Oklahoma City, OK

3d•Onsite

About The Position

As a Staff Data Engineer, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis across our upstream operations. You will be responsible for developing and implementing data pipelines, data integration, and data transformation solutions that support the business as it scales. As a Staff level engineer, you will mentor and inspire high-performing teams. Additionally, this position collaborates with cross‑functional teams to integrate databases with applications, support ETL workflows, and enable scalable cloud-based solutions. The role also includes performance monitoring, capacity planning, disaster recovery preparation, and maintaining comprehensive documentation to support reliable and resilient data operations. In this role within a fast-growing oil and gas company with significant momentum, you will leverage advanced technologies and techniques to design and develop robust data solutions for the business. You will transform raw field, production, and operational data into actionable insights, enabling informed decision-making and driving business growth. By using a broad range of tools, methodologies, and techniques, you will generate new ideas and solve problems, contributing to the overall strategy and objectives of our data team as we lead the company through this next stage of growth.

Requirements

Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent practical experience
7+ years of experience in data engineering, with demonstrated experience designing and scaling production data pipelines.
Hands-on experience with one of the major cloud data warehouse/data lake platforms (e.g., Databricks, Snowflake, or Microsoft Fabric); platform-agnostic mindset preferred, with the ability to ramp quickly on whichever platform the business standardizes on.
Strong proficiency in SQL, Python, and API.
Experience building and orchestrating ETL/ELT workflows using tools such as Azure Data Factory, dbt, Airflow, or equivalent
Solid understanding of data modeling, data warehousing concepts, and distributed data processing
Experience working with cloud platforms (Azure, AWS, or GCP) and cloud-native data services
Demonstrated experience with performance monitoring, capacity planning, and disaster recovery practices for data systems
Strong collaboration skills, with experience partnering across engineering, analytics, and business teams to deliver scalable solutions.
Ability to manage multiple priorities in a fast-paced environment.
Willingness to perform occasional after-hours critical support and maintenance, depending on business needs.

Nice To Haves

Oil and gas industry experience, particularly with upstream data (production volumes, well data, SCADA, or similar).
Familiarity with oil and gas industry software and applications.
Experience integrating or extracting data from oil and gas-specific systems, including SCADA, production accounting, land management, or reserves/engineering applications.
Experience mentoring or providing technical guidance to junior engineers.
Experience with cloud databases (e.g., AWS RDS, Azure SQL Database, Google Cloud SQL) and big data tools (e.g., Hadoop, Spark).
Knowledge of cybersecurity and data protection best practices (Microsoft Entra and other IAM tools)
Understanding of data warehousing, BI tools (e.g., Power BI), and automation scripting (e.g., SQL, Python, Bash, SSRS, KQL, Graph API).

Responsibilities

Design, build, and maintain scalable data pipelines that ingest, transform, and deliver upstream production, field, and operational data to downstream business teams.
Develop and optimize ETL/ELT workflows to support reporting, analytics, and operational decision-making across the business.
Architect and implement data integration solutions that connect source systems (SCADA, ERP, production databases, third-party data feeds) with the company's data warehouse/lake environment.
Demonstrate expertise in data architecture, with a track record of designing scalable, well-structured data models and systems that avoid technical debt and support long-term maintainability.
Collaborate with cross-functional teams, including analytics, engineering, and operations, to translate business requirements into reliable data solutions.
Establish and maintain data quality, validation, and governance standards across pipelines and datasets.
Monitor pipeline and system performance, proactively identifying and resolving bottlenecks, failures, and inefficiencies.
Lead capacity planning efforts to ensure infrastructure scales alongside business growth.
Develop and maintain disaster recovery procedures to support resilient, highly available data operations.
Maintain comprehensive technical documentation for data architecture, pipelines, and operational processes.
Evaluate and recommend tools, platforms, and best practices to continuously improve the data engineering function.
Mentor junior and mid-level engineers, providing technical guidance and supporting their professional growth.
Partner with leadership to align data engineering priorities with the company's broader strategic and growth objectives.