Senior Data Engineer

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.

The Team

Would you like to work in a unique data engineering environment built with modern platforms and data technologies? As a Senior Data Engineer, you get to implement features and enhancements of various sizes and complexities, influencing decisions on every step of the development cycle – from prototype to deployment. Join us on our journey as a member of Enverus' data engineering team working with crew members based in the United States, Canada, and Ukraine.

You would be joining the US Wells Data Engineering Team collecting, transforming, and delivering presentation-ready data to Enverus Prism, our premier energy analytics platform.

Performance Objectives

• Engage and participate in all stages of the Software Development Life Cycle (SDLC) from research, planning, design, development, testing, all the way to deployment
• Analyze, design, and implement low-latency, high-availability, and performant enterprise applications
• Integrate backend data storage including relational database, key-value storage, and document/blob stores
• Evaluate and enhance business requirements based on subject matter expertise and technical feasibility
• Interpret and analyze high volume, heterogeneous data
• Provide pragmatic solutions to complex business problems beyond simple CRUD operations
• Develop reusable, maintainable, efficient, and cost-effective production ready code
• Review and enforce code quality and standards
• Write unit, integration, and end-to-end tests
• Support and monitor infrastructure, application, database, etc.
• Balance working independently with collaborating in a team
• Lead projects and mentor junior team members
• Vigilance in learning new technologies as needed to serve business growth
• Brainstorm and prototype new solutions
• Readily share knowledge through documentation

Competitive Candidate Profile

• Bachelor's degree in computer science or related field
• Minimum 5 years of software development experience
• Excellent communication skills with an advanced English level
• Experience with Python and Spark data transformations
• Firm understanding of the Structured Query Language (SQL)
• Experience with workflow automation and scheduling
• Experience with modern data development tools and libraries
• Experience with modern CI/CD workflows and Infrastructure as Code (IaC)
• Knowledge of big data technologies and ETL processes
• Good command of Git or similar source-control tools
• Firm understanding of data structures and algorithms
• Firm understanding of design patterns and good programming practices
• Familiarity with Agile methodologies
• Familiarity with Airflow, Prefect, or any orchestration service
• Familiarity with cloud infrastructure and concepts

Our Tech Stack

AWS, Databricks, Python, GitHub, Terraform, Microsoft SQL, Azure Data Lake, Airflow, Couchbase, Apache Kafka, Go