Analytics Engineer 2

HDR•Omaha, NE

About The Position

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.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or other applicable degree.
Experience in analytics, BI, or data roles with demonstrated modeling and SQL proficiency.
Ability to translate business requirements into stable, reusable data models.
Familiarity with BI/reporting consumption patterns.

Nice To Haves

In depth experience with dbt Cloud or dbt Core with Git integration.
Experience with BI tools (e.g., Power BI, Tableau) and semantic modeling patterns.
Familiarity with CI/CD for analytics models (PR-based testing, documentation builds, promotion).

Responsibilities

Design and develop analytics models aligned to business processes and reporting needs.
Create curated marts and semantic structures that promote reuse and consistent interpretation.
Partner with analysts to translate business questions into durable data models.
Implement models using layered patterns (raw/conformed/curated) and modular SQL design.
Use managed dependencies and reusable components (e.g., shared functions/templates) for standardized patterns and reuse.
Apply incremental strategies where appropriate to improve refresh performance and manage costs.
Define, document, and maintain KPIs/metrics; ensure consistent definitions across reporting surfaces.
Maintain transformation-layer documentation (model/column descriptions) and publish lineage artifacts for impact analysis.
Contribute to governance practices including naming conventions, tagging/metadata, and change management.
Implement automated tests and freshness checks to ensure the reliability of published analytics datasets.
Participate in peer review and CI checks to improve quality and reduce regressions.
Enable stakeholders through documentation, demos, and shared patterns to support adoption.