Manager, Data Engineering

Plusgrade•Montreal, QC

41d

About The Position

Plusgrade is seeking an experienced Manager of Data Engineering to lead a dedicated team that builds and operates the data infrastructure powering our event-based architecture along with Machine Learning applications and AI-driven products. If youâre passionate about enabling ML and data science teams with robust, scalable, and high-quality data products, and have a proven track record of building and leading high-performing data engineering teams, weâd love to meet you. In this role, youâll manage the development and operation of core data pipelines, feature stores, model-serving integrations, and real-time streaming infrastructure that support ML apps, ML Ops, and data science initiatives. Youâll partner closely with ML engineers, data scientists, and platform teams to ensure that data systems meet the demands of production-grade ML/AI applicationsâbalancing freshness, reliability, scalability, and cost efficiency. Youâll also help define data engineering best practices for the event-driven architecture and the ML use cases, drive adoption of governance and compliance standards, and contribute to shaping Plusgradeâs long-term data strategy. Through servant leadership and strong technical judgment, youâll guide your team while collaborating with product and technical leaders to unlock innovation across the organization. This role reports to the VP of Data Engineering. Our Product Intelligence data engineering team is central to Plusgradeâs product vision, and we take pride in building the foundation that powers our most advanced AI-driven experiences.

Requirements

6+ years in data engineering or related fields, with experience supporting ML/AI and event-streamling workloads.
4+ years in leadership roles managing data engineering teams in fast-paced environments.
Bachelorâs or graduate degree in Computer Science, Software Engineering, or related field (or equivalent experience).
Strong knowledge of modern data engineering practices for ML, including batch and streaming data architectures, feature engineering, and ML data lifecycle management.
Hands-on experience with SQL, Python, cloud-native data platforms (AWS preferred), and distributed systems (Spark, Flink, Kafka, Snowflake, or similar).
Familiarity with ML Ops practices, including feature stores, model versioning, and monitoring frameworks.
At least 1â2 years of hands-on experience using AI and agentic tools (LLMs, agent orchestrators, etc.) in a professional setting is required
Excellent communication and collaboration skills, able to influence across technical and non-technical stakeholders.
Proven ability to recruit, coach, and develop high-performing engineers.
Strong prioritization and execution skills, balancing technical depth with delivery speed.

Nice To Haves

Experience with ML Ops frameworks (e.g., MLflow, Kubeflow, SageMaker).
Knowledge of real-time and event-driven architectures for ML applications.
Familiarity with data governance tools (Atlan, Alation, Collibra) and compliance standards (GDPR, CCPA, SOC 2).
Experience building data platforms to support recommender systems, personalization, or generative AI applications.
Background in CI/CD, infrastructure-as-code, and containerization (Terraform, Kubernetes, Docker).
Exposure to data product management principles, driving adoption and measurable ROI of ML/AI data platforms.

Responsibilities

Lead, inspire, and scale a team of data engineers dedicated to supporting event-streaming, ML/AI and data science workloads.
Define and deliver the data engineering strategy and roadmap for the team and associated projects.
Oversee the design, build, and maintenance of scalable data pipelines, feature stores, real-time streaming systems, and APIs for ML/AI applications.
Partner with application engineers, ML engineers, and data scientists to operationalize models, ensuring reliable training, deployment, and monitoring pipelines.
Establish and monitor SLOs for data freshness, model input quality, reliability, latency, and cost efficiency.
Implement strong practices for data quality, lineage, reproducibility, and versioning of datasets.
Drive adoption of modern orchestration and deployment frameworks (e.g., Airflow, Dagster, Prefect, Kubeflow).
Ensure compliance with data governance, privacy, and security standards across data systems.
Recruit, coach, and develop top engineering talent, fostering a culture of ownership, accountability, and continuous improvement.
Collaborate with product, data science, and ML Ops stakeholders to ensure data systems meet evolving requirements.
Lead incident response and root cause analysis for data issues impacting ML/AI applications.
Contribute to long-term planning for platform scalability, self-service ML data access, and AI-driven innovation.
Define and track KPIs for ML/AI data platform performance, reliability, and team productivity.