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# Data Platform Engineer

- **Company:** Ineos, L.L.C.
- **Location:** Denver, CO, United States
- **Experience:** Expert
- **Salary:** $125,000.0 - $165,000.0
- **Contract:** Permanent contract
- **Skills:** Adaptable Database Systems, Artificial Intelligence, Business Analytics Applications, Microsoft Azure, Software Documentation, Data Architecture, Information Engineering, Data Governance, Data Transformation, Data Systems, Data Warehousing, Supervisory Control and Data Acquisition (SCADA), Jinja (Template Engine), Python (Programming Language), Operational Data Store, SQL Databases, Snowflake, Agentic-AI, Data Layers, Information Technology, Data Analytics, Virtual Agents, Software Version Control
- **Published:** October 19, 2026
- **Apply:** https://ineos.wd503.myworkdayjobs.com/INEOScareers/job/Denver-CO/Data-Platform-Engineer_REQ-3029

## About the Role

· Bachelor's or master's degree in computer science, engineering, data science, or a related field.

· 8+ years of experience in data architecture, data engineering, or a related role, with a focus on the upstream oil and gas sector.

· Proven experience with Snowflake, Microsoft Azure, and common data transformation tools.

· Strong understanding of data modeling, data warehousing, data engineering processes, data governance, and development best practices.

· Strong communication skills, with the ability to articulate technical concepts to diverse audiences.

· Exceptional problem-solving abilities and a strategic mindset.

## Description

We're seeking a skilled Data Platform Engineer to own the models that turn raw operational data into decision-ready analytics. We're a data-driven, AI-enabled energy company, and the quality of our decisions depends directly on the quality of the data layer you'll build. In this role, you'll design, develop, and maintain scalable data models and analytics solutions, working directly with stakeholders to translate complex business requirements into efficient models using dbt, SQL, Python, and other modern data stack tools. The ideal candidate brings deep expertise in data modeling methodologies, data governance, and modern data architecture, with a strong focus on building reliable, production-grade data solutions., * Design and implement robust data models using established methodologies (e.g., Kimball, medallion, etc.) to support analytics, research, and AI agent workloads - balancing accuracy, performance, and ease of adoption by downstream consumers.

 * Build and maintain dbt models with disciplined testing, documentation, and version control, using Jinja and macros to keep transformation logic modular and reusable.
 * Integrate high-volume telemetry and SCADA data, handling the time-series characteristics of field measurement - irregular intervals, late-arriving and backfilled records, sensor gaps, and unit and tag standardization - so operational data is as trustworthy as financial data.
 * Write and optimize complex SQL transformations that meet reporting and analytical requirements while holding up under production load, on cost and on time.
 * Build the semantic and context layers that sit between raw models and their consumers - certified metric definitions, business-friendly naming, documented lineage, and the descriptive context that lets both analysts and AI agents resolve a business question to the right data without guesswork - partnering with stakeholders to keep governance and documentation standards applied across every solution the platform delivers.
 * Own the orchestration and CI/CD pipelines behind the platform, defining job dependencies and schedules and automating testing and deployment so changes reach production safely, repeatably, and without manual intervention.
 * Champion data testing and validation, implementing automated quality checks, monitoring, and alerting so that problems surface before stakeholders find them.