Lead Analytics Engineer: Data Architecture & Standards

Lockton Companies; Kansas City, Northern (MO, KY); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Health insurance

Wellbeing program

Job summary

Lockton Companies in Kansas City, MO seeks a Lead Analytics Engineer to establish the analytics engineering discipline, define data models, and set standards for enterprise data assets. You will lead architecture decisions, mentor engineers, and ensure scalable, governed analytics solutions.

The role emphasizes hands-on design, technical leadership, and continuous improvement in data quality and reuse across the organization.

Qualifications

• Advanced SQL and data modeling experience.
• Experience with enterprise-scale data models.
• Experience implementing business logic and metrics.
• Experience with cloud analytics platforms like Databricks.
• Strong data quality, validation, governance practices.
• Mentor and guide Analytics Engineers.
• Strong analytical and problem-solving skills.

Responsibilities

• Lead design and stewardship of enterprise data products and semantic definitions.
• Define analytics engineering architecture and data patterns.
• Guide design decisions balancing scalability, maintainability, and performance.
• Lead reviews of changes to core data assets.
• Partner with data engineers and stakeholders to deliver scalable data solutions.
• Provide technical direction and prioritize work.
• Mentor team members and promote best practices.

Skills

Advanced SQL

Data modeling

Databricks

Cloud data platforms

Quality governance

Mentoring

Analytical thinking

Tools

Databricks

Job description

Lockton Companies in Kansas City, MO seeks a Lead Analytics Engineer to establish the analytics engineering discipline, define data models, and set standards for enterprise data assets. You will lead architecture decisions, mentor engineers, and ensure scalable, governed analytics solutions.

The role emphasizes hands-on design, technical leadership, and continuous improvement in data quality and reuse across the organization.