Posted yesterday
Senior Data Engineer - ML Platform
@ hireVouch
Remote in Canada
Overview
hireVouch is hiring a Senior Data Engineer for its ML platform team, a greenfield mandate to build data infrastructure for model training, inference and monitoring. The role covers lakehouse architecture on GCP, ML training pipelines, feature stores, observability and data quality, and works closely with Java teams and ML engineers.
Responsibilities:
- Build data lake and lakehouse architecture for ML use
- Build and own ML training data pipelines
- Build offline and online feature stores
- Implement observability, data quality and lineage
- Integrate Kafka event streams with Java teams
Requirements summary:
- 5+ years software development, 3+ in data engineering for ML
- Built ML training pipelines and lakehouse architecture
- Production-grade Python and SQL
- Strong Spark optimization at terabyte scale
- Feature store and MLOps experience
Tools & skills
Source and dates
Source type not verified · cats
- Employer posting date
- 2026-11-05
- First seen by our source
- 2026-11-05
- Source record updated
- 2026-11-06
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Seniority | Senior |
| Department | Data & analytics |
| Experience | 5+ years |
| Work setting | Office |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
| Languages | English |
Summary and details written by AI from the posting; the employer’s posting is the authority.
hireVouch
Saved company profile
Historical company information from a saved The Jobs API profile (LinkedIn/Crunchbase fields). It is not live-verified; figures may have changed. Missing values remain unknown.
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Full company description
Our Edge Hiring and retaining skilled talent is paramount for your company’s success. Finding the right people – who have the right skills and right personality fit – can be incredibly challenging in today’s job market. Here at hireVouch we can help you acquire the highest calibre candidates through the relationships we have formed over more than a decade. Our approach is an ethical one. Rather than use the often-employed aggressive methods typical of the recruitment industry, our focus is placed largely on referral-based networking. Getting it right really matters to us. Let us leverage our network of talent that has already been vouched for by leading professionals in the industry to help your business grow. Services Offered Contingency and Retained Search Here at hireVouch we focus on sourcing best-in-class candidates for rewarding positions in Canada and the US. We have extensive expertise in Contingency, Retained and Temporary placement, primarily within the IT sector. On-Site Search Assistance We have a trusted network of HR and search professionals who can provide on-site search services to help fulfill immediate needs as well as help train internal staff in finding the highest quality talent Company Marketing Strategy We know how competitive the job market can truly be and how important it is to have a strategy to attract talent. At hireVouch we have extensive expertise in helping companies better market themselves. We can provide the tools and knowledge that will assist you in making your business a highly attractive place to work. Developing a company marketing strategy is key to hiring the talent you need to grow.
- LinkedIn followers
- 68,412
- People associated on LinkedIn
- 7
- Reported office locations
- 215 Spadina Avenue, 4th Floor, Toronto, Ontario M5T 2C7, CA
- Other recorded names
- hireVouch
- Profile identifies a recruitment agency
- Yes
Our Edge Hiring and retaining skilled talent is paramount for your company’s success. Finding the right people – who have the right skills and right personality fit – can be incredibly challenging in today’s job market. Here at hireVouch we can help you acquire the highest calibre candidates through the relationships we have formed over more than a decade. Our approach is an ethical one. Rather than use the…
| Field | Value |
|---|---|
| Industry | Staffing and Recruiting |
| Company size | 2-10 employees |
| Headquarters | Toronto, Ontario |
| Organization type | Privately Held |
| Year founded | 2016 |
| Open jobs on Jobsy | 175 |
| View page |
A dedicated data engineer on the ML team, a greenfield mandate. Look for someone who has fed ML training pipelines, not just analytics dashboards.
The role
You sit on the ML team beside Machine Learning Engineers and own the data
infrastructure for model training, inference and monitoring. Today strong software engineers do the data work without lineage, pipeline observability or quality guarantees.
This hire changes that.
The data is structured claims and billing data plus unstructured clinical notes: terabytes, billions of rows, in a medallion lakehouse on GCP.
What you'll own
Data lake architecture: lay out a partly built bronze/silver/gold structure properly for
long-term ML use.
ML training pipelines: extract from siloed stores, transform, and feed structured and unstructured data into training.
Feature stores: build and own both offline and online stores, and know the trade-offs between them.
Observability and data quality: monitoring, alerting, validation and lineage from day one.
Kafka integration: work with Java teams to consume application events into the pipeline reliably.
Quality frameworks: automated checks and schema validation the ML team can trust.
Engineering design: lead architecture decisions and document trade-offs. As much a software role as a data role.
Requirements
5+ years of software development, 3+ focused on data engineering for data science or ML teams.
Built ML training pipelines and lake or lakehouse architecture, not just analytics pipelines.
Production-grade Python and SQL that other engineers can maintain.
Strong Spark, including optimization at terabyte scale (partitioning, broadcast joins, predicate pushdown).
Snowflake, Databricks, Delta Lake or equivalent.
Feature store experience (offline vs online, freshness, training-serving skew); ideally Feast or Tecton.
MLOps basics: lineage, versioning, quality monitoring, leakage prevention.
Kafka or another event-driven source in an ingestion pipeline.
Airflow or equivalent set up from scratch at production scale.
CI/CD, monitoring and alerting as standard practice.
Nice to have: unstructured text pipelines (clinical notes, documents)
· healthcare data such as HL7, FHIR or claims
· RAG or vector search in production · DVC or LakeFS
· GCP (Vertex AI, BigQuery, Cloud Composer, Pub/Sub)
· open-source or published work.
Journal
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