Sr. Data Engineer — AI & Data Science

Senior Data Engineer — AI & Data Science 

About the Role 

B3 enables enterprise manufacturers to digitize their operations and leverage AI to dramatically enhance performance. As the Senior Data Engineer on our AI & Data Science team, you’ll own the team’s analytical data layer: the schemas, views, and pipelines that sit on top of our ingested client data and serve every analytics surface, monitor, and AI feature we build. Client data arrives through our existing ingestion and platform teams — your layer shapes it for analysis, APIs, and AI: designed once to serve many consumers, and kept correct and current. 

This is a high-autonomy role on a small, senior team. You’ll be the AI/DS team’s authority on data architecture, the person we go to with the standing to set the standard for how this layer is designed and run. We’re looking for cemented, production-proven skills: schemas you designed years ago that still hold, pipelines you owned through their failures, and storage decisions that held without needing review. You’ll own a defined, growing data layer with priorities set by one team’s roadmap, an architecture-and-ownership role, not a shared ticket queue. 

About the Work 

• Design and standardize the DS data layer, tables, views, aggregates, and relations built over ingested client data structured so multiple consumers (analytics pages, APIs, LLM-driven features) share the same source of truth without re-cutting the schema for each new one.

• Build and own the pipelines that keep this layer current: transformations, derived-table refresh scheduling, and the monitoring that proves update stability.

• Own the quality bar for derived data: every table in this layer is validated for correctness and update stability before anything is built on it, complementing, not duplicating, the upstream ingestion checks owned by our data and QA teams.

• Bring in external, non-client data sources end-to-end. Think whether, regional, or market signals, sourced, ingested, and structured so we can build correlations that reach beyond client data.

• Drive communication and alignment across the company’s data layers — working collaboratively with our ingestion and platform teams so the stack stays coherent end to end as our services add analytical queries, API serving, and AI-driven reads.

• Work with the ML lead and full-stack engineer to expose the layer as clean, reusable services.

• Evolve the layer as the platform grows, multi-level site structures, corporate-level aggregation, without breaking what’s live.

• No industrial background required. Our domain has its share of complex workflows and terminology, and genuine curiosity is what carries people through it. We’ll help with the rest.

About You 

• 5+ years of data engineering with production ownership, you will be the the accountable owner of schemas and pipelines a business ran on, not a contributor under someone else’s architecture.

• Expert SQL and data modeling: normalization judgment, indexing and performance, view and aggregate design built for multiple consumers.

• Strong pipeline engineering: scheduling, idempotency, failure handling, monitoring and alerting. You know how updates break and design so they don’t.

• Solid Python for data work.

• Self-sufficient by track record: you can point to architecture calls you made alone that held up.

• Works cleanly across team boundaries: you build on upstream data owned by other teams, define clear contracts with them, and keep interfaces documented.

• Clear communicator with non-specialists: your schemas and decisions are documented well enough for a small team to trust without re-deriving them.

Bonus Points 

• Azure stack: Azure SQL, Cosmos DB, App Services, Azure ML.

• Industrial or manufacturing data: historians, sensors, downtime/event data, shift-based operations.

• Experience shaping data for AI/LLM consumers — views and services a model can call safely.

• Experience integrating external/public data sources (weather, market, geographic) into analytical products.

• Near-real-time refresh or CDC/streaming patterns.

• Small-team experience where you owned an entire data layer.

About the Office

• All In, In Office – Centrally located at the corner of Yonge & St. Clair, we show up together, every weekday. Face-to-face time fuels our culture, speed, and creativity.
• Built for Growth – We’re moving fast, keeping structure light, and rewarding people who take ownership and drive momentum.

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