Analytics Engineer

HDR•Omaha, NE

About The Position

The Analytics Engineer will play a critical role in transforming raw data into trusted, analytics-ready datasets that power enterprise reporting, analytics, and business decision-making. This role focuses on building and maintaining well-structured data models using SQL and analytics engineering best practices. The Analytics Engineer sits at the intersection of data engineering and analytics, partnering closely with technical and business stakeholders to ensure data is reliable, well-documented, and scalable.

Requirements

Associates degree in computer science or a related field of study
A minimum of 3 years of experience in software development
Demonstrated analytical thinking and problem-solving skills
Demonstrated ability to work across teams
Experience interpreting business requirements, functional specs and test scripts
Intermediate Microsoft Excel and Word skills

Nice To Haves

Advanced proficiency in SQL for analytics, data modeling, and validation.
Experience designing dimensional data models (facts, dimensions, marts, star schemas).
Experience using dbt for analytics engineering, including testing and documentation.
Familiarity with cloud-based data warehouses such as Snowflake.
Experience with Git-based version control workflows.
Exposure to enterprise BI tools such as Power BI, Tableau, or Looker.
Experience supporting or building standardized metrics and KPI frameworks.
Prior experience helping organizations mature from ad hoc analytics to modeled, metrics-driven analytics.

Responsibilities

Design, build, and maintain analytics ready data models using dbt, following dimensional and semantic modeling best practices (e.g., star schemas, marts, facts, dimensions).
Translate business requirements into clear, well documented data models that are intuitive, performant, and reusable across analytics and BI tools.
Own the analytics layer of the data platform, ensuring consistency, clarity, and trust in metrics and definitions.
Implement dbt tests, documentation, and exposures to improve data quality, observability, and stakeholder confidence.
Partner with analytics, BI, and business teams to define and standardize core metrics and KPIs.
Develop and maintain dbt projects using best practices:
Modular, well structured models
Version control (Git based workflows)
CI/CD and environment promotion patterns
Optimize dbt models for performance, cost efficiency, and scalability within the cloud data warehouse.
Leverage dbt features such as snapshots, seeds, macros, and packages where appropriate.
Participate in code reviews and contribute to shared analytics engineering standards.
Collaborate closely with Data Engineers on upstream data ingestion patterns and source system modeling.
Work with Analytics, Reporting, and Business stakeholders to ensure data models meet analytical and operational needs.
Support BI tools (e.g., Power BI, Tableau, Looker) by providing well designed, analytics ready datasets rather than ad hoc SQL.
Act as a technical leader for analytics engineering and data modeling practices.
Influence data modeling standards, naming conventions, and metric definitions across the organization.
Mentor junior analytics engineers and analysts.
Partner with platform leadership on analytics architecture, governance, and roadmap planning.