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Analytics Engineer I

Job in Kansas City, Jackson County, Missouri, 64101, USA
Listing for: IPFS Corporation
Full Time position
Listed on 2026-08-21
Job specializations:
  • IT/Tech
    Data Engineering, Data Analyst
Salary/Wage Range or Industry Benchmark: 100000 - 150000 USD Yearly USD 100000.00 150000.00 YEAR
Job Description & How to Apply Below

Great companies have great teams.

What started in 1977 as a small, local office offering premium financing has grown to over 600 associates and more than 20locations across North America. In all we say and do, we work towards our mission of providing solutions, resources, and premium financing for the insurance industry.

We work hard to deliver on our corporate mission statement by empowering and encouraging our associates to provide great products and an unparalleled level of service.

Since

1977

Over 600

Associates

29

Locations

Employee Benefits At the Office

Based on your location, we offer parking and commuter benefits programs to help make your commute a little easier.

Insurance Benefits

Our insurance benefits include medical, prescription, dental, voluntary vision, voluntary life/dependent life, group term life, and AD&D.

Our wellness program, Well Works, combined with our Employee Assistance Program (for confidential support), provides support for short and long-term health goals, and any bumps along the way. We also offer gym membership subsidies to support health and fitness goals.

Work Life Balance

We offer paid vacation time and paid personal leave, in addition to company-paid holidays.

Preparing for Your Future

IPFS offers a 401(k) with a company match to help set you up for a financially successful future.

Company Culture

Our associates are the foundation of our company. We strive to make our workplace a great place for everyone. IPFS sponsors company lunches, corporate outings, and even the occasional ice cream truck!

Location: Kansas City, MO (4 days in-office, 1 day remote)
Experience Level: Mid-level (2-4 years)
Department: Data Analytics Team

About the Role

An Analytics Engineer builds and maintains the foundational data infrastructure that transforms raw business data into reliable, analysis-ready insights for decision-making. This position is critical to establishing scalable data models, pipelines, and governance practices that will support our growing analytical needs.

The prime reason for this role's existence is to bridge the gap between raw data ingestion and analytics needs, ensuring that our analysts and stakeholders have access to high-quality, well-documented, and performant data models. This position directly contributes to the company's overall mission by enabling data-driven decision making across all departments, improving operational efficiency, and supporting strategic initiatives through reliable analytics infrastructure.

Key contributions to the company include:

  • Building standardized data models that reduce time-to-insight for business users
  • Implementing data quality and governance frameworks that ensure information is accurate and compliant.
  • Creating reliable, well-documented data pipelines that enable consistent reporting and analytics across all business functions
What You'll Do Data Modeling and Pipeline Development
  • Design, build, and manage data models and ELT/ETL pipelines to transform raw data into structured formats within Snowflake using dbt
  • Create conform and analytics layers that standardize data for business consumption
  • Develop dimensional models and data marts tailored to business requirements
Data Quality and Governance
  • Implement best practices for data quality, integrity, and performance monitoring
  • Contribute to data governance frameworks, including maintaining data lineage and definitions
  • Establish data quality checks and validation processes within dbt workflows
Performance Optimization
  • Optimize data storage and retrieval processes within Snowflake to ensure scalable, reliable, and cost-effective data solutions
  • Fine-tune SQL queries and data transformations for optimal performance
  • Monitor and improve pipeline efficiency and resource utilization
Technical Documentation
  • Create and maintain clear, comprehensive documentation for data models, processes, and key metrics
  • Document data lineage and maintain metadata for analytical datasets
  • Establish documentation standards and best practices for the team
Software Engineering Practices
  • Leverage version control and CI/CD pipelines for streamlined, reliable development processes
  • Ensure code quality through testing, peer reviews, and automated…
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