Lead Data Engineer

Idaho Power Company•Boise, ID

1d•Onsite

About The Position

Idaho Power is seeking a Lead Data Engineer, also referred to as a Lead Data Solutions Developer, to join its Information Technology team in Boise, Idaho. The role provides enterprise-level technical direction for data platforms, solutions, and engineering practices that enable trusted analytics, operational decision-making, and emerging artificial intelligence capabilities. The Lead Data Engineer stewards architecture, defines standards and reusable patterns, guides platform modernization, and turns strategic business needs into scalable, governed, secure, and sustainable data solutions. The position provides leadership through technical direction, coaching, mentoring, facilitation, and influence across teams, without direct reports. The role calls for expertise in enterprise data architecture, cloud data platforms, data engineering, governance, security, and software development practices. The successful candidate will guide platform evolution, address complex technical and production challenges, and communicate architecture strategies, risks, and recommendations to varied audiences. Utility industry experience and familiarity with Microsoft data and analytics technologies are preferred. The position requires extended periods working at a desk or computer and use of office equipment. Occasional lifting of no more than 10 pounds may be required. Idaho Power describes its work as supporting safe, reliable, and affordable energy for communities in Idaho and Oregon. The company advertises work-life balance, competitive pay and benefits, an employee incentive plan, a 401(k) employer match, and a pension plan.

Requirements

Bachelor's degree in computer science, information systems, data science, engineering, or a related field, or an equivalent combination of education and experience.
At least 8 years of experience in data engineering, business intelligence, cloud data platforms, data architecture, database development, or a related discipline.
Knowledge of enterprise data architecture, integration patterns, dimensional modeling, and modern cloud data platform design.
Knowledge of ETL/ELT, orchestration, and data-processing technologies.
Knowledge of Medallion, Lakehouse, and Warehouse architecture patterns.
Knowledge of data governance, metadata, lineage, quality, stewardship, and information lifecycle practices.
Knowledge of data security and access control.
Knowledge of software development lifecycle practices, including Agile, source control, automated testing, and CI/CD.
Advanced SQL and Python development, database design, query optimization, and performance tuning skills.
Ability to design scalable pipelines, integration frameworks, reusable components, and orchestration solutions.
Ability to evaluate technical options and risks, lead reviews, and resolve complex technical, production, data quality, and performance problems.
Experience or skill with Azure DevOps, Git, automated testing, deployment automation, and CI/CD.
Executive and technical communication, consensus building, mentoring, and management of competing priorities.
Ability to translate strategic objectives into scalable, secure, reliable, maintainable, and cost-effective solutions; influence decisions; coach engineers; guide modernization; and drive continuous improvement.

Nice To Haves

Knowledge of utility industry systems, regulations, business processes, and data domains.
Hands-on use of Microsoft Synapse, Microsoft Fabric, Azure Data Factory, Power BI, or comparable enterprise data and analytics technologies.
Experience with utility industry systems, regulations, business processes, or data domains.

Responsibilities

Provide enterprise-level technical leadership and direction for data platforms, solutions, and engineering practices.
Steward data architecture and define technical standards, reference architectures, and reusable patterns.
Guide platform modernization and translate strategic objectives into scalable, secure, reliable, maintainable, and cost-effective solutions.
Lead architecture, design, and code reviews.
Evaluate platform capabilities, architectural tradeoffs, technical risks, and emerging technologies.
Resolve complex production, data quality, and performance issues, and lead resolution of systemic or critical challenges.
Coach and mentor engineers and influence decisions across teams without direct reports.
Build consensus and communicate strategies, risks, and recommendations to executive and technical audiences.
Balance business value, architecture, security, performance, cost, and delivery risk.
Drive continuous improvement, anticipate future needs, and remain accountable for the health and evolution of enterprise data capabilities.