Lead Data Engineer

Overview

The Lead, Data Engineer leads the Data Engineering team and provides technical direction for the data infrastructure, pipelines, and systems that support reporting, analytics, and data products across the organization. This role combines people leadership, technical expertise, and business partnership to ensure data solutions are reliable, scalable, and aligned with organizational priorities.

The Lead, Data Engineer remains hands-on technically while guiding the work of the team and partnering with Finance, Data & Analytics, and other business leaders to translate business needs into practical data solutions.

Responsibilities

Team Leadership & Delivery

• Lead, mentor, and develop the Data Engineering team, establishing clear expectations for performance, quality, and ownership.
• Manage team priorities, workload, and delivery in alignment with broader business objectives.
• Provide technical guidance and oversight for engineering work, including architecture, design, and production readiness.
• Serve as an escalation point for complex technical, data quality, and delivery challenges.

Data Engineering & Architecture

• Design, build, and maintain scalable data pipelines, integrations, warehouses, and supporting infrastructure.
• Establish and maintain engineering standards for data architecture, modeling, reliability, performance, and maintainability.
• Remain hands-on in development and resolution of complex or business-critical technical issues.
• Drive improvements to automation, tooling, monitoring, and engineering practices that strengthen the data environment.

Strategy & Business Partnership

• Establish technical direction and priorities for Data Engineering in alignment with organizational objectives.
• Partner with Finance and other business and technical stakeholders to translate data needs into effective solutions.
• Evaluate technologies, processes, and architectural approaches that improve the organization's ability to use and scale data.
• Communicate technical strategy, risks, recommendations, and infrastructure needs to technical and business leadership.

Additional Expectations

Demonstrates and promotes the company's Impact Behaviors in all interactions and decision-making.

• Design
• Ownership
• First Team
• Performance

Additional information

Maintains a professional, collaborative, and client-focused approach while supporting team and organizational objectives.

Performs other duties, responsibilities, and special projects as assigned to meet business needs.

Qualifications

Required

• 7+ years of related data engineering experience, including 2+ years leading, supervising, or providing technical direction to others.
• Bachelor's degree in Computer Science, Data Engineering, a related discipline, equivalent technical training, or equivalent professional experience.
• Strong experience designing, building, and maintaining data pipelines, ETL/ELT processes, data warehouses, and related infrastructure.
• Advanced SQL skills and strong programming experience with Python, Java, Scala, or a comparable language.
• Strong understanding of data architecture, modeling, scalability, performance, reliability, and data quality practices.
• Experience with modern cloud data platforms, orchestration frameworks, and data processing technologies.
• Demonstrated ability to remain hands-on technically while leading and developing a team.
• Strong business acumen with the ability to translate business requirements into practical technical solutions.
• Strong communication and stakeholder management skills across technical and non-technical audiences.

Preferred

• Experience with AWS, Azure, or Google Cloud Platform.
• Experience with orchestration and transformation tools such as Airflow, dbt, or similar technologies.
• Experience with distributed or streaming data technologies such as Kafka or Spark.
• Experience with infrastructure-as-code and CI/CD practices for data environments.
• Experience building, scaling, or maturing a Data Engineering function.

Key Performance Indicators

• Reliability and availability of critical data pipelines and infrastructure.
• Timeliness and quality of Data Engineering deliverables.
• Data quality and reduction of recurring production issues.
• Progress against Data Engineering roadmap and priorities.
• Team performance, development, and delivery effectiveness.
• Effectiveness of data solutions in supporting business and stakeholder needs.

Work Environment

• While performing the essential duties of this job, the individual is regularly required to speak and hear
• While performing the essential duties of this job, the individual is regularly required to use hands for keyboarding, gross manipulation, and fine manipulation
• Works at a desk and computer screen for extended periods of time
• Works in a traditional climate controlled office environment or from home
• Works in a highly stressful environment dealing with a wide variety of challenges, deadlines and diverse employee population