Senior Data Engineer

HDR•Omaha, NE

12d

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.
Minimum 6 years of data engineering experience with demonstrated delivery of complex pipelines and data products.
Strong SQL proficiency and experience with modern ELT tooling and patterns.
Experience implementing testing, monitoring, and operational support practices.

Nice To Haves

Experience leading dbt adoption and enforcing project standards (layering, naming conventions, testing, documentation) across teams.
Experience with streaming or near-real-time ingestion patterns.
Experience collaborating with governance/security stakeholders on production controls.

Responsibilities

Design scalable pipelines and transformation strategies for high-volume and complex domains.
Define model patterns and conformed entities to enable consistent downstream consumption.
Perform design reviews and guide technical decisions for maintainability and governance.
Implement advanced transformation patterns such as incremental processing, snapshots (as applicable), and reusable libraries.
Establish project-level conventions for layering (raw/conformed/curated) and naming standards.
Drive adoption of documentation and lineage to support impact analysis and safer change.
Define a testing strategy and ensure appropriate coverage for critical models and metrics.
Implement monitoring/alerting and data quality dashboards to detect issues early.
Lead incident response and root cause analysis; improve resilience through preventative controls.
Implement CI/CD patterns for transformations (build/test/docs) and enforce review discipline.
Partner with platform/security to ensure access, tagging/classification, and governance alignment.
Mentor and coach engineers through reviews, pairing, and knowledge transfer.