Analytics Engineer I

Imperial PFS; Kansas City (MO); Hybrid; USD 90,000 - 130,000; Full time

Job summary

Imperial PFS is seeking an Analytics Engineer to design and maintain scalable data infrastructure, enabling reliable analytics across the organization. You will build data models, pipelines, and governance to support decision-making and reporting.

In this role, you will bridge raw data ingestion and analytics needs, ensuring high-quality, well-documented data assets and efficient pipelines. The position involves collaboration with analysts and business stakeholders in a hybrid work setting.

Qualifications

• 2–4 years of analytics engineering, data engineering, or similar role.
• Advanced ANSI-SQL proficiency for querying and transforming data.
• Snowflake expertise with architecture and best practices.
• Strong data modeling skills for analytics.
• dbt proficiency for managing data transformations in Snowflake.
• Detail-oriented with a commitment to quality and continuous improvement.
• Strong communication and collaboration with non-technical stakeholders.
• Curiosity to understand business problems through data.

Responsibilities

• Design, build, and manage data models and ELT/ETL pipelines in Snowflake using dbt.
• Create conform and analytics layers for standardized data consumption.
• Develop dimensional models and data marts tailored to business needs.
• Implement data quality checks, governance, lineage, and definitions.
• Optimize storage, queries, and pipeline performance for cost-effective solutions.
• Create clear documentation for data models, processes, and metrics.
• Collaborate with analysts and stakeholders to translate requirements into technical data solutions.
• Participate in CI/CD and code reviews to ensure code quality.

Skills

Advanced SQL

Snowflake

dbt

Data modeling

Communication

Analytical thinking

Tools

Fivetran

Tableau

Looker

Power BI

Python

Job description

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 deployment

Collaboration and Stakeholder Management

• Work closely with analysts and business subject matter experts to understand requirements
• Translate business needs into effective technical data solutions
• Participate in regular stakeholder meetings to refine and expand data capabilities

What You Bring

Required Qualifications

• 2-4 years of experience in analytics engineering, data engineering, or similar role
• Advanced SQL proficiency in ANSI‑SQL for querying, data transformation, and building data infrastructure
• Snowflake expertise with hands‑on experience in its architecture and best practices for data management and processing
• Strong data modeling skills with deep understanding of data modeling principles and experience designing efficient structures for analytics
• dbt proficiency for managing data transformations and building data models within Snowflake
• Passion for detail and quality – skilled at spotting data and process gaps and committed to driving continuous improvement
• Strong communication skills and comfort collaborating with non‑technical stakeholders
• Intellectual curiosity and drive to understand business problems through data

Preferred Qualifications

• SnowPro certification or equivalent advanced Snowflake expertise
• Experience with modern cloud data architecture (data lakes, data lakehouses, cloud data platforms)
• Experience with ELT/ETL tools (Fivetran experience a plus)
• Knowledge of data visualization tools (Tableau, Looker, Power BI)
• Previous experience in insurance, finance, or regulated industries
• Python programming experience for data parsing, transformation, and scripting within data pipelines

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