Staff Data Engineer

Key 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.

Required Qualifications

• 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
• 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