Analytics Engineer I: Build Scalable Data Pipelines

Imperial Funding Corporation; Kansas City (MO); Hybrid; USD 90,000 - 120,000; Full time

Benefits offered by this job

Parking and commuter benefits

Insurance benefits

WellWorks wellness program

Gym membership subsidies

Paid vacation

Paid personal leave

Company-paid holidays

401(k) with company match

Company lunches / outings

Ice cream truck

Job summary

Imperial Funding Corporation seeks an Analytics Engineer I to design and maintain data infrastructure that delivers analysis-ready insights. You will build scalable data models, ELT/ETL pipelines, and governance practices to support data-driven decisions across departments.

The role bridges raw data ingestion and analytics, ensuring high-quality, well-documented data for stakeholders, with standard collaboration across the analytics team.

Qualifications

• 2-4 years in analytics engineering, data engineering, or similar role.
• ANSI-SQL proficiency for querying and data transformations.
• Snowflake expertise and best practices for data management.
• Strong data modeling skills for analytics.
• dbt proficiency for managing data transformations.

Responsibilities

• Design, build, and manage data models and ELT/ETL pipelines in Snowflake using dbt (Data Modeling & Pipeline Development).
• Create conform analytics layers and data marts tailored to business needs.
• Implement data quality, integrity, and governance checks.
• Optimize data storage and SQL performance in Snowflake.
• Document data models, lineage, and metadata; maintain CI/CD for code quality.
• Collaborate with analysts and stakeholders to translate requirements into data solutions.

Skills

Experience 2-4 years

ANSI SQL

Snowflake

Data modeling

dbt

Attention to detail

Communication

Curiosity

Tools

Fivetran

Tableau

Looker

Power BI

Python

Job description

Imperial Funding Corporation seeks an Analytics Engineer I to design and maintain data infrastructure that delivers analysis-ready insights. You will build scalable data models, ELT/ETL pipelines, and governance practices to support data-driven decisions across departments.

The role bridges raw data ingestion and analytics, ensuring high-quality, well-documented data for stakeholders, with standard collaboration across the analytics team.