Data Engineer I

Yes Energy•Boston, MA

2d•Hybrid

About The Position

Join the Market Leader in Electric Power Data and Analytics Solutions The electrical grid is the largest and most complicated machine ever built. Yes Energy’s industry-leading electric power trading analytics software provides real-time visibility into the massive amount of data generated by the North American electrical grid daily. Our unique and innovative view of the data informs real-time trading decisions and mid-to-long-term investment decisions that keep utility prices low, support the energy transition, and keep the grid running. It’s both challenging work and work with a purpose. Be a part of our successful, growing business during international transformation. Position Summary At Yes Energy, we help our clients win the day ahead through Better Data, Better Delivery, and Better Direction. The Data Quality team ensures the data powering our products is accurate, complete, timely, and trusted across complex market datasets. Reliable data enables our customers to make informed decisions that support grid reliability and critical market operations. This Data Engineer I role will support the development of data quality processes, monitoring, automation, issue analysis, and root cause investigation across Oracle, Snowflake, and related data systems. This role is ideal for someone early in their data engineering career who has strong SQL skills, a curious mindset, and an interest in building reliable, scalable data processes. The person in this role will help the team identify repeating data issues, analyze trends, diagnose root causes, and recommend ways to improve data quality at the source. A key part of this role will also be helping automate manual or repetitive data quality workflows. This may include improving monitoring processes, building queries or scripts to detect issues earlier, supporting data validation automation, and helping create repeatable processes that make the team more efficient. The ideal candidate is curious, detail-oriented, analytical, and excited to learn how complex energy market data moves through our systems, where it can break, and how we can make it more reliable for our customers. They should also be comfortable using practical AI-assisted tools and techniques to support analysis, documentation, triage, automation, and process improvement where appropriate.

Requirements

1+ years of experience with SQL and relational databases
Creative problem-solving abilities
Analytical and troubleshooting skills
Experience building or maintaining production data systems
Demonstrated ability to design and build frameworks or reusable systems, not just individual fixes
Strong analytical skills and comfort working with complex, high-volume datasets
Ability to communicate technical findings clearly to both technical and non-technical audiences.
Experience using AI or ML-assisted techniques for data analysis, monitoring, or automation
Ability to embrace a customer service mindset

Nice To Haves

Experience with data quality or data observability frameworks
Experience with Oracle, Snowflake, or PL/SQL.
Familiarity with data governance or quality scoring approaches
Experience with time-series, market, or energy data
Familiarity with Agile development methodologies

Responsibilities

Build and improve foundational data quality and observability frameworks across Oracle, Snowflake, and related data systems, including reusable checks, monitoring queries, alerts, reporting, and documentation that help the team detect, investigate, and prevent recurring data issues.
Identify repetitive data quality tasks and automate them using SQL, scripts, scheduled jobs, AI-assisted tools, or other practical solutions.
Help identify trends in recurring data issues and support root cause analysis to understand why problems are happening.
Developing and maintaining data quality checks, monitors, alerts, and reporting.
Use practical AI-assisted techniques to help with issue triage, pattern detection, documentation, and analysis where appropriate.
Partner with Data Collections, Engineering, Support, and Product teams to investigate and resolve data quality issues.
Help improve existing data quality processes by recommending ways to reduce repeated issues and improve reliability.
Support the creation and maintenance of documentation around data quality findings, known issues, checks, and processes.
Help analyze customer-reported data issues and provide clear findings that support timely and effective resolution.
Participate in support and on-call rotations as needed to support clients by answering complex data questions, providing timely and effective solutions, so that we can empower our clients to make informed decisions