Sr. Software Engineer, Product
Senior product engineer at Enurgen (utility-scale solar analytics) building customer-facing dashboards, workflows, and big-data visualizations on fleet-scale time-series telemetry backed by ClickHouse, plus loss-detection and real-time operations features. React front end, Node/Python backend.
- Compensation
- CA$155K–CA$180K / yr
- Location
- Ottawa
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Software engineering
- Posted
Skills
Must have
- 5+ years building production software, ideally including data-intensive web applications
- Strong systems thinker; architect solutions before reaching for code
- Solid React (or equivalent) for front-end work
- Node, Python, or similar for backend
- Comfort with SQL and reasoning about query cost on very large tables
- Track record of building greenfield software in a startup-shaped environment: small team, fast iteration, real customers
- Strong written and verbal communication, including with non-engineering stakeholders
Nice to have
- OLAP / columnar database experience (ClickHouse especially, or BigQuery, Snowflake, DuckDB)
- Large-scale time-series data and the visualization techniques that handle it
- GraphQL schema design at production scale
- deck.gl, Mapbox, or other geospatial/WebGL visualization
- Real-time / streaming experience: Kafka, Redpanda, Flink, Materialize, or similar
- Experience shipping the same software to both cloud and on-prem deployments
- Openness to occasional travel
Full description
Compensation: $155k – $180k CAD
About Enurgen
Enurgen is building DUET, a generational platform for utility-scale solar that turns raw fleet telemetry into recovered revenue. We close the gap between what conventional monitoring can see and what it can diagnose, and across a portfolio it adds up to a lot.
At our core is DUET (DUal-sided Energy Tracer), a 3D, physics-based energy yield model spun out of SUNLAB at the University of Ottawa and built on 10+ years of R&D. The DUET Platform runs per-timestep model-vs-field comparisons across the full 30-year asset lifetime, from design through construction to operations, automating detection, prioritization, and reporting at the component level. An AI agent on top enriches the analysis with context and patterns across portfolios.
The software engineering work
The work is heavy on data, and this role is where that data becomes something someone can act on. You'll build the dashboards, workflows, and visualizations sitting on top of fleet-scale time-series, and you'll own how fast they feel. The people opening those dashboards (Asset Managers, PV engineers, operations teams, executives) depend on this software to act on what they're seeing.
What you'll do
- Build and evolve the product surface of DUET: the dashboards, workflows, and visualizations our customers use daily
- Develop big-data visualizations that stay responsive at fleet scale, working against our ClickHouse-backed analytics layer
- Extend loss attribution and issue detection workflows in collaboration with our R&D team, turning physics output into an operator's next action
- Build the operator-facing side of our shift into real-time, portfolio-wide operations as our streaming capabilities evolve
- Work directly with our customers (Asset Managers, PV engineers, operations teams, executives) when the problem benefits from it
- Have a real say in product direction. You'll sit close enough to both the data and the customer to have an informed opinion, and we want it
What we're looking for
Required
- 5+ years building production software, ideally including data-intensive web applications
- Strong systems thinker. AI changed the leverage, so you architect solutions before reaching for code
- Solid React (or equivalent) for front-end work, and Node, Python, or similar for backend
- Comfort with SQL and reasoning about query cost. Our UI sits on very large tables and you'll be querying them directly
- Track record of building greenfield software in a startup-shaped environment: small team, fast iteration, real customers