saint laurent, qc data engineer Jobs – 32 Openings This Week

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- Azure
- Research
- SQL
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- Paid time off
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Senior AI Data Engineer– Agentic Healthcare Platform

Medeloop
Montréal, QC

Quick apply

Job details

Skills

Ontology
Azure
Big data
Spark
Google Cloud Platform
Research
SQL
AWS
Data management
Natural language processing
AI
Python

Full job description

The Role

This is a full-ownership data engineering role at the center of Medeloop's AI platform. You won't be maintaining pipelines someone else built, you'll be architecting the data backbone that powers AI agents doing real operations at scale. You'll work directly with data scientists, AI engineers, and product teams to turn raw, complex healthcare data into the clean, structured, semantically-rich foundation our AI scientists depend on. Your work shows up in customer products and research outcomes, not internal dashboards that no one reads.Candidates who currently perform these tasks exclusively through manual processes are unlikely to be suitable for this role. We require an individual who has already adopted and integrated AI techniques to enhance operational velocity, rather than one who is contemplating future experimentation.If you want to build something that genuinely changes how medical research gets done, this is the role.

What You'll Own

The healthcare data lake: curating, extending, and evolving it through new concepts, derived variables, and data models that directly inform our AI engines and customer products
AI-native data workflows: designing and operating AI-powered pipelines (using tools like Claude Code and agent frameworks) to automate harmonization, cleaning, quality checks, and summarization at scale
NLP and semantic infrastructure: building pipelines for entity extraction, concept normalization, embedding-based retrieval, and semantic search that power the AI Scientist platform
Novel data extraction approaches: experimenting with and building new methodologies for working with unstructured clinical data, not just applying existing playbooks
Research-grade data products: delivering analytical samples, cohorts, and final datasets that withstand scientific scrutiny and are actively used by researchers and customers
Data governance and observability protocols: including access controls, PHI/PII handling, data classification, compliance, monitoring, alerting, data freshness, and comprehensive documentation to enable self-service capabilities.

What We're Looking For

3+ years of relevant data engineering or data management within an analytics-driven organization, with end-to-end ownership from raw ingestion to final data product
Deep hands-on experience with healthcare CDMs (OMOP, FHIR, PCORnet) — designing or extending them, not just querying
Knowledge of medical ontologies: UMLS, SNOMED CT, RxNorm
Experience with big data, data pipelines and tooling that support retrieval-augmented generation (RAG), vector integrations, embedding workflows, and other AI/ML workloads. Experience in big data tooling such as Spark, Iceberg, EMR
Fluent in Python and SQL; comfortable across structured and unstructured data
Proven NLP experience: semantic search, entity recognition, concept normalization, embedding pipelines
Strong grasp of inferential statistics and cohort methodology to be a real partner to data scientists and customers (as part of onboarding)
Experience contributing to an AI/ML product, especially in automated research or scientific discovery
Experience mentoring other engineers and providing technical leadership

Bonus Points

Multi-cloud experience (AWS, Azure, GCP)
Authorship or contribution to peer-reviewed publications or technical reports

Why Medeloop

Ownership from day one: small team, high-trust, no layers between your work and its impact
Technically ambitious: you'll build AI-powered workflows, not just support them
Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter
Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

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