Job Opportunities API

About our Company Equilibrium Energy is a team of technologists, power market experts, and AI pioneers reimagining how the world’s most critical industry operates. We’re building a first-of-its-kind AI operating system for the power sector, uniting cutting-edge science with real-world purpose to enable a cleaner, more resilient energy future. At EQ, you’ll join a tight-knit group of brilliant, curious, and adventurous people who bring the same energy to collaboration as they do to innovation. Equilibrium Energy is a well-funded, Series B clean energy startup backed by some of the most prominent institutional investors in climate. New colleagues will share our vision that a next-generation energy company must be built from the ground up on deep industry expertise combined with an unwavering commitment to modern digital approaches.  We’re looking for collaborative, talented, passionate and resourceful folks to join our team and help us lay the foundation for our important mission and ambitious plan. What we are looking for Equilibrium Energy is building the platform that will power the clean energy transition. As a Staff/ Sr Staff Software Engineer- Data Engineering , you will play a critical role in shaping our long-term data architecture. You’ll lead the design and implementation of high-impact data initiatives that support energy trading, forecasting, and AI development. This is a hands-on role that blends platform engineering, advanced data processing, and cross-functional collaboration. You’ll help scale our systems to enable near real-time decision-making and empower traders, data scientists, and operators to move faster with confidence. What you will do Design and implement the long-term data architecture using modern technologies and frameworks. Build and maintain scalable ETL/ELT pipelines in Python, SQL, and dbt—ingesting data via APIs, web scraping, and streaming sources. Develop and operate data pipelines using orchestration frameworks such as Temporal and Dagster. Design data models and schemas for our cloud warehouse (Databricks) and relational databases; contribute to the development of our ML feature store. Optimize workflows for performance and cost efficiency. Drive large, cross-functional data initiatives from planning to execution. Partner with AI and engineering teams to ensure high-quality datasets for machine learning and analytics. Collaborate with product managers, scientists, and engineers to gather requirements and deliver robust data products. Mentor other engineers in best practices for data ingestion, architecture, and scalable pipeline design. Support the software testing cycle, debug code, and resolve issues found during QA or user acceptance testing. The minimum qualifications you’ll need Bachelor’s degree in Computer Science, Data Science, Engineering, or a related technical field. 7+ years of progressive experience in data or software engineering. Advanced programming skills in Python and SQL. Experience building globally distributed data systems and real-time pipelines. Hands-on with orchestration/stream processing tools like Temporal, Dagster, Airflow, Spark, or Kafka. Strong knowledge of relational and NoSQL databases (e.g., Postgres, MySQL, MongoDB, ElasticSearch, Cassandra). Familiarity with data warehousing and cloud computing (Databricks and AWS preferred). Experience mentoring engineers and providing architectural direction. Strong analytical skills, with the ability to work with unstructured or ambiguous datasets. Commitment to data quality, testing, and observability. Experience with both OLTP and OLAP data processing systems Nice to have additional skills Experience with energy market data or weather data sources (e.g., NWS, NOAA, Yes Energy). Experience using dbt for transformations and data quality checks. Collaborating with data science teams to build and productionize ML