Data Engineer – Renewable Energy Analytics & BI

Primoris Services Corporation; Denver (CO); Hybrid; USD 80,000 - 105,000; Full time

Benefits offered by this job

Paid Company Holidays

Paid Time Off

Medical, Dental, Vision

401K with matching contribution

Job summary

Primoris Services Corporation, a leader in energy EPC, seeks a Data Engineer for the Renewable Energy division in Denver. You will design and maintain data pipelines, data models, and BI solutions to support pursuit, estimating, engineering, procurement, and construction operations.

Key tools include Databricks, Power BI, Python, and SQL. You will collaborate with estimators, engineers, project managers, and operations leaders to convert historical project experience into predictive insights

Qualifications

• Bachelor's degree in a related field and 3+ years of data engineering or BI experience.
• Experience with Databricks, Python, SQL, and Power BI.
• Proficiency in building data models for analytics and reporting.

Responsibilities

• Design, develop, and maintain data pipelines and scalable data models.
• Develop and maintain Power BI dashboards and reporting solutions.
• Collaborate with estimators, engineers, and project leaders to drive data-driven decisions.

Skills

Databricks

Power BI

Python

SQL

GitHub

Excel

Data Modeling

ETL/ELT Development

Azure Data Platform

REST APIs

AI-Assisted Development

Machine Learning Fundamentals

Education

Bachelor's degree in Computer Science, Data Engineering, Construction Management, Engineering, Data Science, Information Systems, or related field

Tools

Databricks

Power BI

Python

SQL

GitHub

Azure Data Platform

Job description

Primoris Services Corporation, a leader in energy EPC, seeks a Data Engineer for the Renewable Energy division in Denver. You will design and maintain data pipelines, data models, and BI solutions to support pursuit, estimating, engineering, procurement, and construction operations.

Key tools include Databricks, Power BI, Python, and SQL. You will collaborate with estimators, engineers, project managers, and operations leaders to convert historical project experience into predictive insights