Data Engineer

About us: Founded in 1990, LS Power is a premier development, investment, and operating company focused on the North American power and energy infrastructure sector, with leading platforms across generation, transmission and energy expansion solutions. Since inception, LS Power has developed or acquired 50,000 MW of power generation, including utility-scale solar, wind, hydro, battery energy storage, and natural gas-fired facilities. Through its transmission business, LS Power Grid, the company built and operates 780+ miles of high-voltage transmission and 7 transmission utilities and has another 375+ miles currently in construction or development. LS Power actively invests in and scales businesses that are meeting the growing needs of the energy expansion, including electric vehicle charging, demand response, microgrids, renewable fuels and waste-to-energy platforms. Over the years, LS Power has raised more than $76 billion in debt and equity capital to support North American infrastructure.

Our Purpose is to solve complex energy problems that improve the world

Our Mission is to make lives better by developing a cleaner and more reliable energy ecosystem

Our Values are the willingness to participate in and help strengthen our culture of integrity, Innovation, Teamwork, and Taking Ownership

Our People create value and are our Most Valuable asset. We take our values of Integrity, Innovation, Teamwork and Taking Ownership seriously and ask candidates to think about how they can help us further enhance our culture with their specific skillsets, capabilities and experiences.

We provide our team the opportunity to share their unique perspectives, solve new challenges, and continue in their career growth. We are committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits that include 100% employer paid premium healthcare, paid parental leave and more.

The salary for this role is $140,000-$160,000. This is the range that we in good faith anticipate relying on when setting wages for this position. We may ultimately pay more or less than the posted range and this range is only applicable for jobs to be performed in New York. This salary range may also be modified in the future.

This position is responsible for the design, maintenance, and reliability of the data infrastructure that supports our energy marketing and trading operation. You will the responsibility for the data layer behind a growing portfolio of internal business applications, ensuring that analysts, traders, and developers across the organization have timely access to clean, well-organized data.

This is a hands-on, technical role embedded in a small, high-output software team. The ideal candidate is someone who thinks of data engineering not as an end in itself but as a means of solving real business problems. We value simplicity, clarity, and maintainability over architectural complexity.

- Manage and extend a Microsoft Fabric environment including OLTP SQL Databases, lakehouses, warehouses, data pipelines, notebooks, and dataflows, ensuring reliable scheduled refreshes and data quality across workspaces.

- Support data flows that underpin internal trading, settlements, and reporting applications.

- Build and maintain data ingestion pipelines from a wide variety of sources: SFTP feeds, REST APIs, email attachments, flat files, and third-party energy market data vendors.

- Own the operational reliability of data infrastructure: monitor pipeline health, troubleshoot failures, manage credentials and permissions, and handle workspace administration including item ownership transfers.

- Develop applications that provide direct transparency into the underlying data that feeds complex analysis.

- Manage ML training and inference pipelines that influence trading and plant management decisions.

- Use master data management architectures alongside interactive applications to retain and reinforce critical business knowledge.

Qualifications

- Have 5+ years of hands-on experience in data engineering, database administration, or a related data-intensive role.

- Are proficient in SQL and have meaningful experience managing relational databases in production, whether SQL Server, PostgreSQL, or similar.

- Have worked with a modern cloud data platform such as Microsoft Fabric, Databricks, Snowflake, or Azure Synapse, and understand the patterns of lakehouse and warehouse architectures.

- Write Python comfortably for data pipeline development, automation, and scripting, and understand how to keep scripts clean, modular, and easy for others to maintain.

- Have built data ingestion from real-world messy sources: APIs with pagination and rate limits, SFTP servers, email inboxes, inconsistently formatted files.

- Believe that the best data infrastructure is the kind nobody has to think about because it just works, and you take pride in building systems that are boring in the best sense of the word.

- Are a strong communicator who can translate between technical implementation and business requirements without losing fidelity in either direction.

- Have a heavy bias to get to the end state through planning and action. Actions like performing the migration/refactor and dropping dead code and dead datasets.

Ideally, you have...

- Experience in the energy sector, particularly with energy marketing and trading data such as LMP pricing, SCADA, ISO market feeds, or commodity futures and derivatives.

- Familiarity with energy market data vendors such as Bloomberg, ICE, Platts, NRG Stream, Databento, or similar.

- Background in finance, gas, or commodities even if not specifically in power markets.

- A demonstrated habit of building tools, automations, or personal projects at high volume — we value people who are prolific builders and problem solvers, not just people who have worked on one large system.

- Active use of AI-assisted development tools and a thoughtful perspective on how to integrate them into a professional workflow responsibly.

- Personal experience hosting, fine-tuning, or managing language models, or involvement in a technically ambitious personal project involving AI infrastructure.

- An appreciation for software principles like KISS, DRY, and keeping complexity to a minimum — you would rather delete code than add it.