Data Platform Engineer: Energy Analytics & AI-Ready Data

ineos; Denver (CO); On-site; USD 140,000 - 190,000; Full time

Job summary

INEOS Energy in Denver is seeking a Data Platform Engineer to own data models transforming raw operations data into analytics-ready data. You will design scalable models using dbt, SQL, Python and other tools, partnering with stakeholders to meet business needs and ensure governance.

The role emphasizes production-grade data solutions, with responsibilities spanning modeling, testing, orchestration, and data quality across the data stack.

Qualifications

• 8+ years of experience in data architecture or data engineering with upstream oil & gas focus.
• Proven experience with Snowflake, Microsoft Azure, and data transformation tools.
• Strong understanding of data modeling, warehousing, governance and development best practices.

Responsibilities

• Design robust data models to support analytics, research and AI workloads.
• Build and maintain dbt models with testing, documentation and version control.
• Integrate high-volume telemetry and SCADA data with time-series handling.
• Write and optimize complex SQL transformations for reporting and analytics.
• Develop semantic/context layers with certified metrics and clear lineage.
• Own orchestration and CI/CD pipelines for production deployments.
• Champion automated data quality checks, monitoring and alerting.

Skills

Data modeling

SQL

Python

dbt

Snowflake

Azure

Data governance

Data architecture

Education

Bachelor's or master's in CS/Engineering/Data Science

Tools

Snowflake

Microsoft Azure

dbt

Job description

INEOS Energy in Denver is seeking a Data Platform Engineer to own data models transforming raw operations data into analytics-ready data. You will design scalable models using dbt, SQL, Python and other tools, partnering with stakeholders to meet business needs and ensure governance.

The role emphasizes production-grade data solutions, with responsibilities spanning modeling, testing, orchestration, and data quality across the data stack.