Senior Back-End / Data Engineer – Innovation team

About Us

Power Factors is accelerating the green energy transition by providing advanced analytics and AI insights to operators of renewable energy assets. Our SaaS platforms are used to manage over 250 GW of wind, solar, hydro, and energy storage projects globally. By driving down operational costs and increasing revenue, we are tackling one of the world’s most important challenges: making renewable energy the world’s leading source of power. Our vision is to create a sustainable world powered by renewable energy. Our mission is to fight climate change with code.

About the Role

We are looking for a Senior Backend Engineer to join the Innovation team— This role is the connective tissue of the . You will own the data foundation that makes state-of-the-art model training possible, the inference service that makes model outputs usable, and the platform integration that puts those outputs in front of pilot customers. From production ETL through to shadow-mode validation pipelines, you will be the engineer who keeps every track moving. The role is based in Montreal and is critical-path from day one.

About You

You are a senior backend engineer with serious data engineering chops and a track record of shipping production ML-adjacent systems. You have built pipelines that don’t just move data — they guarantee its quality, version it for reproducibility, and hold up under real-world operational chaos. You understand that a model is only as good as the data it trains on, and that an inference service is only as useful as the product it integrates into. You are energised by breadth and ownership,



and you are comfortable carrying a heavy load on a small team where your decisions have direct programme-level consequences.

Required Qualifications

- 6+ years of backend and data engineering experience, with a proven track record of shipping production systems
- Production-grade ETL/ELT pipeline design at scale: idempotency, retry logic, backfill jobs, incremental loading, and cost-controlled warehouse compute
- Schema design and data modelling across heterogeneous sources — experience reconciling signals from disparate systems into a canonical, queryable format
- Data quality engineering: automated quality gates (sparsity, flatline detection, outlier flagging, freshness checks), alerting pipelines, and dataset versioning for ML reproducibility
- API design and development: RESTful inference services with contract testing, latency and throughput budgeting, and structured observability (logs, metrics, traces)
- Experience integrating ML model outputs into SaaS product surfaces: auth and authorisation, customer isolation, feature flag management
- Cloud infrastructure proficiency (AWS preferred), containerisation (Docker, Kubernetes), and CI/CD pipeline ownership
- Python and SQL as core tools; hands-on experience with up-to-date warehouse technologies (Snowflake, BigQuery, or Databricks)
- Pipeline orchestration with Airflow, Prefect, Dagster, or equivalent




- Excellent communication skills and fluency in English (written and verbal)

Beneficial Qualifications

- Experience with time-series, IoT, or industrial sensor data (SCADA systems, irregular sampling, high-missingness signals) — a significant advantage
- Familiarity with streaming data platforms (Kafka, Kinesis, or Pub/Sub) for real-time or near-real-time ingestion
- Experience designing and managing feature stores for ML training and serving
- Renewable energy domain knowledge: understanding of wind and solar asset operations and the data they produce
- Experience standing up shadow-mode or A/B comparison pipelines for ML systems — running live inference in parallel with an existing path and logging predictions against actuals
- Multi-tenancy and platform integration experience in B2B SaaS products
- Knowledge of data lake architectures and open table formats (Delta Lake, Iceberg, Parquet)
- Familiarity with MLOps practices: model registry conventions, retraining triggers, drift monitoring

Your Responsibilities

Data Foundation

- Design and build the production ETL pipeline from source systems to warehouse and feature store at fleet scale, covering thousands of wind and PV sites across multiple OEMs
- Own canonical signal schema design across wind and PV asset classes and OEMs — the deepest technical unknown in the programme and the foundation everything else depends on
- Implement automated data quality gates: missingness, flatline detection, outlier flagging, and freshness checks, with alerting that generates tickets automatically
- Implement dataset versioning sufficient to reproduce every trained model from scratch
- Build and maintain backfill

📌 Senior Back-End (Brossard)
🏢 Power Factors
📍 Brossard