Data Engineer

About the role

In clinical research, the data that should tell you whether a trial is on track is almost never in one place — it's scattered across spreadsheets, shared drives, document repositories and legacy systems, each with its own format and its own version of the truth. MAESTRO exists to consolidate clinical and operational data into one validated, audit-ready platform, and the data layer is what makes that promise real.

You'll build and own the governed data layer that ingests all of that, keeps it trustworthy, and powers live trial visibility, forecasting and sponsor-ready reporting. Because MAESTRO is used in regulated environments, your pipelines aren't just "move data from A to B" — every number has to be traceable, defensible and reproducible, held to the same standards we sell to our customers.

This role sits at the intersection of clinical operations, quality and engineering, with real ownership over how trial data flows through the platform.

What you'll do

• Build and operate ingestion and transformation pipelines that bring clinical and operational data in, validate it, and normalize it into a structured, trustworthy model.
• Lead validated data migrations out of spreadsheets, shared drives, document repositories and legacy systems into a clean, governed structure — without losing or corrupting a single record.
• Design data models and warehousing that support live dashboards, enrollment and site-performance forecasting, and sponsor- and inspection-ready reporting.
• Implement data quality, lineage and observability so that every figure on a dashboard can be traced back to its source and explained to an auditor.
• Partner with security and governance on data classification, residency and retention, ensuring data stays in-region and within policy.

What you'll bring

• 4+ years in data engineering, building and operating production pipelines.
• Strong data manipulation and scripting skills, and hands-on experience with a modern data stack (orchestration, transformation and warehousing).
• A solid grasp of data modelling, performance and reliability.
• A rigorous, detail-obsessed approach to data correctness — you treat a silent data error as a serious bug, not a rounding issue.

Nice to have

• Experience with regulated or clinical data and data-residency constraints.
• Familiarity with predictive analytics or machine-learning feature pipelines.
• Exposure to streaming or event-driven data.

Education

• A degree in Computer Science, Data Science, Engineering, Statistics, Mathematics or a related quantitative field from a recognized post-secondary institution — or equivalent practical experience that demonstrably matches the level of the role.
• Internationally educated candidates are welcome; foreign credentials should be assessed for Canadian equivalency (e.g. WES, ICAS or a comparable recognized service).
• An asset (not required): coursework or certification in data engineering, analytics or a modern data stack.

Location & eligibility

This role is open only to candidates who are based in Canada and legally entitled to live and work in Canada (Canadian citizens, or permanent/legal residents with valid Canadian work authorization). We are not able to sponsor relocation or work authorization for this position.

Why join

You'll turn scattered, untrustworthy trial data into a single source of truth that helps research teams catch problems weeks earlier than they could before. Your pipelines won't be a back-office afterthought — they're a core part of a product that customers are inspected on, and they'll be held to that same bar.