Data Warehouse Engineer

Hamilton Company•Reno, NV

2d•Onsite

About The Position

Hamilton Company is seeking a hands-on Data Warehouse Engineer to develop data pipelines, integrations, and business systems that connect information and support business decisions. The role combines data engineering, analytics, and technical business analysis, translating business requirements into working solutions. Using SQL, Python, and machine learning methods, the engineer will build and maintain ETL/ELT pipelines, connect databases and applications, and contribute to systems for capturing, organizing, and analyzing business information. The position is onsite in Reno, Nevada.

Requirements

Strong SQL skills for querying, transforming, validating, and troubleshooting relational data.
Strong Python skills for data processing, analysis, and automation.
Foundational understanding of data warehousing, relational data modeling, and ETL/ELT concepts.
Foundational understanding of machine learning concepts and interest in applying them to record linkage, forecasting, and classification.
Strong problem-solving skills and ability to work independently on assigned tasks while seeking guidance when needed.
Clear communication skills and ability to collaborate with technical and business colleagues.
Bachelor’s degree.
At least 2 years of relevant experience in data engineering, data warehousing, business intelligence, analytics, or technical business analysis.
Must pass drug and background screens.
May be required to wear employer-provided Personal Protective Equipment.

Nice To Haves

Experience with Power BI and DAX.
Exposure to record matching, deduplication, forecasting, or classification through work, coursework, or personal projects.
Experience connecting applications or databases through APIs.
Familiarity with version control, automated testing, and deployment workflows.

Responsibilities

Build and maintain ETL/ELT pipelines integrating data from databases, business applications, and newly developed systems.
Contribute to data warehouse development by implementing and maintaining tables, data models, and analytical datasets with architectural guidance.
Work with business stakeholders and the technical manager to understand workflows, clarify requirements, and define technical tasks.
Develop integrations, automation, and business system components to improve how information is captured, connected, and used.
Help implement record-linkage and entity-resolution solutions using rules-based, probabilistic, and machine learning approaches; investigate uncertain matches and validate results.
Prepare datasets and help develop, test, and evaluate forecasting and classification models.
Integrate approved model outputs into data pipelines, business systems, dashboards, and reporting workflows.
Implement data validation and reconciliation checks; investigate discrepancies and troubleshoot pipeline and integration issues.
Collaborate with the technical manager and DevOps engineer to test, deploy, and monitor solutions using established engineering practices.
Document data flows, business rules, integration logic, and changes.
Take ownership of assigned deliverables, communicate progress and blockers, and incorporate feedback from technical reviews.