Analytics Engineer

At HDR, our employee-owners are fully engaged in creating a welcoming environment where each of us is valued and respected, a place where everyone is empowered to bring their authentic selves and novel ideas to work every day. As we foster a culture of inclusion throughout our company and within our communities, we constantly ask ourselves: What is our impact on the world?

Watch Our Story:' https://www.hdrinc.com/our-story'

Each and every role throughout our organization makes a difference in our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your community, but around the world.

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.

Data Modeling & Analytics Engineering
*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.

dbt Development & Platform Practices
*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.

Cross Functional Collaboration
*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.

Senior Level Expectations
*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.

Preferred Qualifications
* 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.