Staff Data Engineer - 26267
Description
We are the most trusted energy-dedicated SaaS company, with a platform built to maximize value from generative AI, and our innovative solutions are reshaping the way energy is consumed and managed. By offering anytime, anywhere access to analytics and insights, we’re helping our customers make better decisions that help provide communities around the world with clean, affordable energy.
The energy industry is changing fast. But we’ve continued to lead the way in energy technology, creating intelligent connections across the entire energy ecosystem, from renewables, power and utilities, to oil and gas and financial institutions. Our solutions create more efficient production and distribution, capital allocation, renewable energy development, investment and sourcing, and help reduce costs by automating crucial business operations. Of course, this wouldn’t be possible without our people, which is why we have built a team of individuals from a diverse range of backgrounds.
Are you ready to help power the global quality of life? Join Enverus, and be a part of creating a brighter, more sustainable tomorrow.
Our Data Engineering team builds and maintains the pipelines that manufacture and deliver energy-related data powering Enverus's customer-facing applications. We sit at the intersection of data infrastructure and domain expertise, transforming raw energy and geoscience data into reliable, production-grade assets that our customers depend on every day.
We are looking for a Staff Data Engineer who combines strong hands-on engineering with the ability to collaborate across engineering, QA, and product management teams.
You will join a collaborative, fast-moving team of data engineers who own the full lifecycle of data manufacturing — from ingestion through transformation and delivery. We move quickly and hold a high bar for code quality and system design. Engineers here work on meaningful problems across cloud infrastructure, pipeline architecture, and AI integration.
Performance Objectives
Contribute as a staff developer on the data engineering team, collaborating to deliver on priorities developed in conjunction with the product management group.
Build and maintain scalable data manufacturing pipelines using Databricks, PySpark, and modern ETL patterns.
Integrate new energy data sources into existing pipelines and applications, and modernize data manufacturing processes for improved reliability.
Drive the integration of LLM and GenAI technologies into data extraction and transformation workflows.
Competitive Candidate Profile
Proficiency with ETL pipeline development using Databricks/Unity Catalog and PySpark.
Experience with data modeling, lineage tracking, and schema management.
Familiarity with CI/CD and DevOps practices for data pipelines using GitHub.
Hands-on experience with cloud technologies including AWS S3, IAM, and Lambda.
Experience with relational databases such as SQL Server or PostgreSQL.
Skills that stand out
Experience integrating LLM or GenAI technologies into data workflows.
Familiarity with Airflow for pipeline orchestration.
Knowledge of Kubernetes or ArgoCD for infrastructure and deployment automation.
Background in energy or geoscience domain data.
Our Technology Stack
Databricks, Unity Catalog, PySpark, Python, AWS S3, AWS IAM, AWS Lambda, SQL Server, PostgreSQL, GitHub, Airflow, ElasticSearch, ArcGIS, Kubernetes, ArgoCD