Software Engineer- Data Engineering (Staff/ Sr Staff)

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.
• Committed to building high-quality, scalable data systems.