We are looking for a Data Engineer to drive the design and delivery of scalable data solutions that support critical business objectives within the oil and gas sector. This position is based in King of Prussia, Pennsylvania, and combines technical execution with leadership responsibilities, including guiding a small team and shaping engineering best practices. The ideal candidate brings strong expertise in modern data platforms, builds dependable pipelines and models, and works comfortably across complex source systems to produce high-quality data assets.

Responsibilities:

• Lead the development of enterprise data pipelines and modeling solutions that enable reliable reporting, analytics, and operational decision-making.
• Provide day-to-day technical direction for a small team of data engineers, offering mentorship, code guidance, and support for delivery priorities.
• Design, build, and optimize ETL workflows using Python and SQL to move and transform data from a wide range of upstream systems.
• Create and maintain scalable data structures in Snowflake and Databricks to support performance, usability, and long-term maintainability.
• Establish engineering standards, documentation practices, and development approaches that improve consistency and quality across data initiatives.
• Collaborate with business and technical stakeholders to translate data needs into practical architecture and implementation plans.
• Contribute directly to hands-on coding, testing, troubleshooting, and deployment activities across the data engineering lifecycle.
• Evaluate data quality, resolve integration challenges, and improve pipeline reliability through monitoring and continuous enhancement.
• Demonstrated experience in data engineering with a strong background in designing and implementing ETL solutions.
• Proficiency in Python and SQL for data transformation, automation, and performance tuning.
• Hands-on experience with Snowflake and Databricks in production data environments.
• Familiarity with Microsoft Fabric and its use in modern data platform ecosystems.
• Proven ability to integrate data from multiple source systems and consolidate it into scalable data models.
• Experience leading technical workstreams or mentoring entry-level engineers within a small team setting.
• Strong understanding of data architecture, pipeline optimization, and engineering best practices.

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