Description

Why YOU want this position

At Enverus, we're committed to empowering the global quality of life by helping our customers make energy affordable and accessible to the world.

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.

Enverus has a dynamic hub for developing software in Brno, Czech Republic, and you can learn more about our team, company culture, and benefits here.

Our Data Engineering team builds and maintains the pipelines that manufacture and deliver energy-related data powering Enverus's customer-facing applications. Following a major data acquisition, we are expanding our team to integrate a large library of well log and subsurface geology data into our existing infrastructure — standardizing pipelines, resolving overlaps across massive datasets, and building new downstream connections into our applications. Longer term, this work extends into AI-assisted data extraction and integration with Enverus's AI-enabled platform.

We are looking for a Senior Data Engineer who can work independently on complex, ambiguous problems and bring strong instincts for modern pipeline architecture and AI-assisted data workflows.

The Team:

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, with real ownership over what they ship and how it evolves.

What you will do:

• Build and maintain scalable data manufacturing pipelines using Databricks or equivalent MPP ETL platforms, PySpark, and modern ETL patterns.
• Integrate new energy and geoscience data sources into existing pipelines and applications.
• Standardize and consolidate data pipelines across large, overlapping datasets as part of a major data integration effort.
• Incorporate LLM and GenAI technologies into data extraction and transformation workflows, including AI-assisted well log digitization.
• Work independently to resolve ambiguous or complex technical challenges, engaging teammates for input as needed.
• Mentor junior engineers and help define engineering standards for the team.
• Understand how your work fits the broader team picture and surface conflicting requirements to management and peers.

What skills you should have:

• Proficiency with ETL pipeline development using Databricks, Snowflake, or a comparable MPP data platform, and PySpark.
• Experience with data modeling, lineage tracking, and schema management.
• Hands-on experience with pipeline orchestration tools such as Airflow, Prefect, or similar.
• 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, including tools such as Amazon Bedrock and Claude Code.
• Experience with infrastructure as code tools such as Terraform or equivalent.
• Familiarity with 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, Prefect, Amazon Bedrock, Claude Code, ElasticSearch, ArcGIS, Kubernetes, ArgoCD, Terraform

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