About the job

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: massive analytical queries over fleet-scale time-series, real-time streaming pipelines feeding live analytics, and dashboards that have to stay fast and responsive. 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 our ClickHouse-backed analytics layer — schema design, raw SQL, performance work on tables that don't get smaller

• Develop big-data visualizations that stay responsive at fleet scale

• Extend loss attribution and issue detection workflows in collaboration with our R&D team — this is where DUET's physics meets the operator's day-to-day

• Evolve our real-time streaming capabilities — live analytics, operator-facing workflows, and the data backbone behind our shift into real-time, portfolio-wide operations

• Help shape how we collect data on-site as latency requirements push us closer to the equipment — a direction we're actively exploring, not a fixed roadmap

• Work directly with our customers — Asset Managers, PV engineers, operations teams, executives — when the problem benefits from 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 — you architect solutions before reaching for code

• Comfort with raw SQL and analytical query design — you reach for EXPLAIN before reaching for an ORM

• Solid React (or equivalent) for front-end work, and Node, Python, or similar for backend

• 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

Strong pluses — any one of these moves you up the list

• OLAP / columnar database experience — ClickHouse especially, or BigQuery, Snowflake, DuckDB

• Real-time / streaming experience — Kafka, Redpanda, Flink, Materialize, or similar

• Large-scale time-series data and the visualization techniques that handle it

• Experience shipping the same software to both cloud and on-prem deployments

• Performance-sensitive systems work; HPC or low-latency backgrounds welcome

• Proficiency in a lower-level language such as C++, Rust, or Go

• On-site or edge data collection experience — OPC-UA subscriptions, industrial protocols, or shipping software into industrial environments. Useful but very much not required.

Not required

• A solar or energy background. The domain is interesting — utility-scale plants are complex engineered systems and our customers are sophisticated. You'll pick it up; we don't expect you to walk in with it.

How we work

• Small, fast increments. Getting something real in front of operators usually beats polishing it longer in isolation.

• Tight feedback loops. We learn from short experiments with real customers, not speculative roadmaps.

• Focus over breadth. Fewer things, done well.

• Engineers paired with coding agents. AI is part of how we write code, debug, document, and analyze data — built into the workflow, not bolted on the side.

• High-touch and Slack-driven. Threads carry the work async, huddles when priorities move fast. No meetings to fill calendars.

• Small enough that nobody hides. Everyone ships to production, and everyone talks to customers.

• Engineering and R&D at the same table. The decisions are sharper for it.

Location

Remote or hybrid. Office in Ottawa, Ontario, Canada.

Openness to occasional travel is a nice to have.