Senior Data Engineer (Energy Data)

About the role

Enverus is expanding its Data Engineering team to integrate a large library of well log and subsurface geology data into its existing infrastructure. The Senior Data Engineer will build and maintain scalable data manufacturing pipelines, standardize overlapping datasets, and incorporate AI‑assisted workflows.

Key responsibilities

• 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.

Required profile

• Proficiency with ETL pipeline development using Databricks, Snowflake, or comparable MPP data platforms and PySpark.
• Experience with data modeling, lineage tracking, and schema management.
• Hands‑on experience with pipeline orchestration tools such as Airflow or Prefect.
• 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.

Required skills

• Databricks
• Snowflake
• PySpark
• Python
• AWS S3
• AWS IAM
• AWS Lambda
• SQL Server
• PostgreSQL
• GitHub
• Airflow
• Prefect
• Amazon Bedrock
• Claude Code
• ElasticSearch
• ArcGIS
• Kubernetes
• ArgoCD
• Terraform