Lead Analytics Engineer

Lockton; Kansas City (MO); On-site; USD 150,000 - 210,000; Full time

Job summary

Lockton in the United States is seeking a Lead Analytics Engineer to establish the Analytics Engineering discipline, shaping data modeling patterns and implementation standards while staying hands-on in design and implementation.

You will guide architectural decisions, mentor Analytics Engineers, and collaborate with Data Engineers and business stakeholders to deliver scalable data solutions enterprise-wide.

Qualifications

• Advanced SQL and data modeling experience.
• Experience designing and maintaining enterprise-scale analytical data models.
• Experience implementing business logic, metrics, semantic definitions, and reusable data assets.
• Experience with modern cloud-based analytics platforms (e.g., Databricks).
• Strong understanding of data quality, validation, testing, and governance practices.
• Experience mentoring technical team members and leading solution design.
• Strong analytical and problem-solving skills.
• Ability to balance business needs with maintainability and scalability.

Responsibilities

• Lead the design and stewardship of enterprise data products, analytical data models, shared metrics, semantic definitions, and reference data.
• Define and evolve the Analytics Engineering architecture, data modeling patterns, and implementation practices the team follows.
• Guide technical design decisions and evaluate tradeoffs related to scalability, maintainability, quality, performance, and long-term support.
• Establish review practices for significant changes to core data assets, metrics, dimensions, and business rules.
• Establish and evolve standards for data modeling, transformations, testing, validation, documentation, naming conventions, and semantic definitions.
• Promote consistent use of standards and reusable assets through technical reviews, guidance, and mentorship.
• Establish quality control and change management practices that improve the reliability and maintainability of data assets.
• Lead complex troubleshooting and root cause analysis efforts.
• Partner with Data Engineers, Analytics Engineers, and business stakeholders to deliver scalable and sustainable data solutions.
• Provide technical direction on solution design, implementation approaches, and technical prioritization.
• Identify opportunities to improve development processes, architecture, reuse, and long-term supportability.
• Ensure business logic, metrics, and semantic definitions are implemented consistently across data assets and analytical models.

Skills

Advanced SQL

Data modeling

Governance

Mentorship

Analytical thinking

Problem solving

Tools

Databricks

Job description

Your Responsibilities

The Lead Analytics Engineer is responsible for establishing the Analytics Engineering discipline. This role defines and evolves the data modeling patterns, business logic frameworks, and implementation standards that guide the team's work. You remain hands‑on in design and implementation, providing technical direction and serving as the authority on complex data decisions.

Key Responsibilities

Enterprise Data Products & Architecture

• Lead the design and stewardship of enterprise data products, analytical data models, shared metrics, semantic definitions, and reference data.
• Define and evolve the Analytics Engineering architecture, data modeling patterns, and implementation practices the team follows.
• Guide technical design decisions and evaluate tradeoffs related to scalability, maintainability, quality, performance, and long-term support.
• Establish review practices for significant changes to core data assets, metrics, dimensions, and business rules.

Standards, Governance & Quality

• Establish and evolve standards for data modeling, transformations, testing, validation, documentation, naming conventions, and semantic definitions.
• Promote consistent use of standards and reusable assets through technical reviews, guidance, and mentorship.
• Establish quality control and change management practices that improve the reliability and maintainability of data assets.
• Lead complex troubleshooting and root cause analysis efforts.

Analytics Engineering Delivery

• Partner with Data Engineers, Analytics Engineers, and business stakeholders to deliver scalable and sustainable data solutions.
• Provide technical direction on solution design, implementation approaches, and technical prioritization.
• Identify opportunities to improve development processes, architecture, reuse, and long-term supportability.
• Ensure business logic, metrics, and semantic definitions are implemented consistently across data assets and analytical models.

Leadership

• Provide technical leadership, mentorship, and guidance to Analytics Engineers.
• Serve as the technical escalation point for complex Analytics Engineering challenges.
• Help establish a culture of technical discipline, thoughtful design, and continuous improvement.

Qualifications

• Experience working within insurance, brokerage, financial services, or other complex data-intensive industries.
• Advanced SQL and data modeling experience.
• Experience designing and maintaining enterprise-scale analytical data models.
• Experience implementing business logic, metrics, semantic definitions, and reusable data assets.
• Experience working with modern cloud-based analytics and data platforms, such as Databricks.
• Strong understanding of data quality, validation, testing, and governance practices.
• Experience mentoring technical team members and leading solution design efforts.
• Strong analytical and problem‑solving skills.
• Ability to balance business needs, maintainability, scalability, and long-term support considerations.

Success Looks Like

• Shared business logic is reused rather than recreated.
• Standards and processes are consistently followed across the discipline.
• Data models, business rules, and semantic definitions remain maintainable and well documented.
• Analytics Engineers have clear technical direction and guidance.
• New solutions are built using common patterns rather than one‑off approaches.
• Enterprise metrics, dimensions, and business rules remain consistent across data assets.
• Analytics Engineering practices continue to scale as adoption and demand grow.
• Data assets are easier to maintain, support, and evolve over time.