Full Lifecycle Data Engineer

Lockton; Kansas City (MO); On-site; USD 110,000 - 160,000; Full time

Job summary

Lockton is seeking experienced Full-Lifecycle Data Engineers to design, build, and operate a next-generation data platform across ingestion, transformation, modeling, serving, and production operations in Kansas City. You will combine software, data, and analytics engineering to deliver scalable data products powering analytics, applications, and machine learning.

You will contribute to building reliable, scalable data pipelines and services, enabling self-serve analytics and integrated data

Qualifications

• 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.

Responsibilities

• Build and maintain scalable batch and streaming data pipelines.
• Design ETL/ELT transformations and modular data workflows.
• Design and maintain data warehouses and lakehouse architectures.
• Build data services or APIs for downstream consumers.
• Own scheduling, orchestration, and data observability.
• Collaborate with analytics, product, and engineering teams.

Skills

Programming

SQL

Data modeling

ETL/ELT

Databricks

Azure

Data warehousing

Orchestration tools

Distributed systems

Backend data systems

Tools

Databricks

Azure

Airflow

CI/CD

Job description

Your Responsibilities

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.

Key Responsibilities

Data Ingestion & Integration

• 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

Data Processing & Transformation

• Design and implement transformation pipelines using ELT/ETL patterns


• Develop modular, reusable data transformations (Databricks experience a plus)


• Ensure data consistency, correctness, and reproducibility

Data Storage & Modeling

• 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

Data Products & Serving Layer

• 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

Orchestration & Reliability

• Own scheduling and orchestration systems


• Implement monitoring, alerting, and data observability practices


• Debug and resolve end-to-end data issues across the stack

Collaboration & Enablement

• 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

Qualifications

• Strong programming skills


• AdvancedSQLand 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

Nice to Have

• Experience buildingdata 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)

Soft Skills

• Comfortable owning ambiguous, end-to-end problems


• Clear communication with both engineers and business users