Senior Data Engineer - ML Platform

hireVouch; Canada; Remote; CAD 140,000 - 190,000; Full time

Job summary

hireVouch is seeking a Senior Data Engineer, ML Platform to own the data infrastructure for training, inference and monitoring. You will shape the lake architecture, build end-to-end ML pipelines and own both offline and online feature stores, ensuring data quality and lineage from day one.

You will collaborate with ML Engineers, push for robust MLOps practices, and drive architecture decisions. Expect a software-focused data role with production-scale systems and strong emphasis on reliability.

Qualifications

• 5+ years of software development, 3+ focused on data engineering for ML teams.
• Built ML training pipelines and lakehouse architectures, 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).
• 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.

Responsibilities

• Own the data infrastructure for model training, inference and monitoring.
• Data lake architecture: lay out bronze/silver/gold structure for ML use.
• ML training pipelines: extract, transform and feed data for training.
• Feature stores: build offline and online stores and know their trade-offs.
• Observability and data quality: monitoring, alerting, validation and lineage.
• Kafka integration: work with Java teams to consume events into pipelines.
• Quality frameworks: automated checks and schema validation for ML data.
• Engineering design: lead architecture decisions and document trade-offs; a software role as well as a data role.

Skills

Software development

ML data engineering

Python

SQL

Spark

MLOps basics

CI/CD

Monitoring

Data quality

Data lineage

Tools

Kafka

Airflow

Snowflake

Databricks

Delta Lake

Feast

Tecton

Job description

Senior Data Engineer, ML Platform

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.

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.