Full Lifecycle Data Engineer

Lockton•Kansas City, MO

About The Position

We are looking for experienced Full-Lifecycle Data Engineers to design, build, and operate our next-generation data platform. This role owns the full data lifecycle—from ingestion and transformation through modeling, serving, observability, and production operations. Successful candidates combine software engineering, data engineering, and analytics engineering skills to deliver reliable, scalable data products that power analytics, applications, and machine learning.

Requirements

Strong programming skills
Advanced SQL and data modeling expertise
Experience with data processing frameworks
Hands-on experience with cloud platforms (Azure)
Experience with modern data warehouses
Familiarity with orchestration tools
Understanding of distributed systems and data architecture patterns
Ability to build both backend-style data systems and analytics pipelines
Comfortable owning ambiguous, end-to-end problems
Clear communication with both engineers and business users

Nice To Haves

Experience building data APIs or internal data services
Infrastructure-as-code
CI/CD for data systems
Experience with ML data pipelines or feature stores
Front-end exposure for dashboards or internal tools
Data governance enablement
Security and privacy (HIPAA)

Responsibilities

Build and maintain scalable batch and streaming data pipelines
Integrate data from APIs, event streams, databases, SaaS tools, and third-party systems
Ensure reliable, fault-tolerant ingestion across multiple sources
Design and implement transformation pipelines using ELT/ETL patterns
Develop modular, reusable data transformations (Databricks experience a plus)
Ensure data consistency, correctness, and reproducibility
Design and maintain data warehouses, lakes, and lakehouse architectures
Build analytics-ready data models (star schema, wide tables, semantic layers)
Optimize data structures for performance and cost efficiency
Build data services or APIs that expose curated datasets to downstream consumers
Enable self-serve analytics via BI tools and semantic modeling layers
Support embedded analytics or product-facing data features when needed
Own scheduling and orchestration systems
Implement monitoring, alerting, and data observability practices
Debug and resolve end-to-end data issues across the stack
Partner with analytics, product, and engineering teams to define data needs
Translate business requirements into scalable data solutions
Support experimentation, reporting, and machine learning workflows