Senior Data Solutions Engineer - Commercial DT, Onsite Houston

ConocoPhillips; Houston (TX); On-site; USD 120,000 - 170,000; Full time

Benefits offered by this job

Competitive base pay

Bonuses and incentives

Retirement plan

Paid time off

Job summary

ConocoPhillips is seeking a Senior Data Solution Developer for Houston to bridge raw energy market data with commercial insights. You’ll design data pipelines, models, and automated data assets, collaborating with data architects, analysts, and model developers to support forecasting and risk modeling.

You will build scalable data infrastructure, enforce data governance, and pursue continuous automation while staying current with industry trends and best practices in data engineering.

Qualifications

• Legally authorized to work in the United States.
• 3+ years of full stack Python development experience.
• 3+ years working with commercial or front office users in energy trading.
• Strong knowledge of SQL and cloud-based data platforms.

Responsibilities

• Design and develop robust data pipelines, ETL/ELT processes, and workflows.
• Collaborate with data architects to translate requirements into data solutions.
• Build scalable data infrastructure including warehouses and lakes.
• Implement data quality validation and monitoring frameworks.
• Monitor pipelines, troubleshoot failures, and automate recovery.

Skills

Python

SQL

Cloud platforms

Airflow

REST APIs

GitHub Actions

Docker

Education

Bachelor's degree in CS or related field

Tools

Databricks

Snowflake

Spark

Kafka

Hive

StreamSets

ADF

Job description

ConocoPhillips is seeking a Senior Data Solution Developer for Houston to bridge raw energy market data with commercial insights. You’ll design data pipelines, models, and automated data assets, collaborating with data architects, analysts, and model developers to support forecasting and risk modeling.

You will build scalable data infrastructure, enforce data governance, and pursue continuous automation while staying current with industry trends and best practices in data engineering.