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Verse is building an energy intelligence platform for the AI economy, helping large energy consumers achieve faster, cheaper, and cleaner power through real-time control of energy assets and complete visibility into their energy portfolio. The company is backed by Bessemer Venture Partners, GV, Coatue, and NVIDIA.
As a Software Engineer in Data Engineering, you will be a member of the technical staff designing, building, and maintaining the next generation of data platform tools, pipelines, and orchestration frameworks. This role supports Core Engineering needs and serves multiple teams and products across diverse data types at Verse. You will act as a technical leader with advanced data engineering expertise, vetting proposals, solutions to internal and customer-facing problems, and system designs while driving implementation with the highest standards of software engineering best practices.
Key responsibilities include ensuring data quality, contracts, and integrity with best practices around access, governance, and federation throughout the data lifecycle. You will implement and maintain best practices in the data code repository, write shared libraries and reusable data tools across various data flows, sources, and sinks, and partner closely with Data Science and data-heavy internal teams to support analytical and AI/ML workflows. You will foster a culture of well-designed systems, test-driven software, and proactive communication with transparency and mutual respect.
Required qualifications include advanced software development skills in Python, Rust, Java, Scala, or similar data-oriented languages; deep understanding of databases, data lake/warehouse architectures, and data pipeline solutions in cloud-native environments; proficiency in handling large, complex data in both streaming and batch processing across transactional and analytical settings; commitment to delivering high-quality software while adhering to best practices; continuous learning about contemporary technologies; and discipline using generative AI tools.
Preferred qualifications include a Bachelor's or Master's degree in Computer Science, Data Science, or related field; senior-level software development talent; proven track record as a technical leader in high-performing data engineering teams; intimate knowledge of scaling big data solutions in cloud environments; hands-on experience with tools like Hadoop, Spark, Airflow, DBT, Dagster, Temporal, Presto/Trino, Iceberg, BigQuery, and others; experience building event-driven architectures with streaming tools; knowledge of managed data solutions like Databricks and Snowflake; practical knowledge of data formats like Parquet and Avro; experience supporting AI/ML teams; and comfort building internal dashboards and data visualizations.
