Full time

Callière Group; Toronto; On-site; CAD 135,000 - 165,000; Full time

Job summary

Callière Group in the Greater Toronto Area is hiring a Senior Data Engineer to lead the design and evolution of real-time data systems and scalable architecture supporting operational intelligence and predictive modelling.

You’ll own end-to-end pipelines from ingestion to ML enablement, applying statistical methods and working across operations, finance, and product to translate complex requirements into robust data platforms.

Qualifications

• Proven experience building and scaling data platforms in production environments.
• Deep expertise in streaming systems and distributed data processing.
• Strong system design skills at architecture and implementation levels.
• Solid grounding in statistical modelling and real-world applications.
• Bias toward ownership, pragmatism, and building reusable systems.

Responsibilities

• Architect and operate real-time and batch data pipelines (Kafka, Spark, AWS).
• Lead the evolution of a scalable lakehouse architecture for analytics and ML workloads.
• Design and productionize end-to-end ML pipelines, including feature engineering and real-time inference.
• Apply statistical methods (time-series, regression, anomaly detection) to business problems.
• Partner with operations, finance, and product to translate requirements into robust systems.
• Own IaC-based infrastructure design to ensure reproducibility and scale.
• Drive performance, reliability, and observability across data systems.
• Evaluate and introduce new tools and patterns across data engineering and applied AI.

Skills

Data platform design

Streaming systems

System design

Statistical modelling

Ownership mindset

Tools

AWS

Apache Spark

Kafka

Terraform

Delta Lake

Job description

Greater Toronto Area, Canada | Posted on 06/19/2026

• Salary Up to $150,000 CAD + Bonus + Perks

Job Description

We’re partnered with a capital-backed operator in the industrial / infrastructure space that’s investing heavily in building a modern data platform from the ground up. They’re hiring a Senior Data Engineer to lead the design and evolution of real-time data systems and scalable architecture supporting both operational intelligence and predictive modelling.

This is a high-ownership role at the intersection of data engineering, applied statistics, and production AI. You’ll operate as a senior individual contributor with end-to-end responsibility across pipelines, platform design, and ML enablement.

Why This Role Is Compelling

• True ownership of the data platform: architecture, pipelines, and production ML systems.
• Direct impact on operational performance and financial strategy, not just analytics.
• Senior-level autonomy with influence over technical direction and tooling choices.
• Lean, execution-focused team where senior engineers build and ship complete systems.
• Long-term stability paired with a builder environment and modern stack.

Scope and Responsibilities

• Architect and operate real-time and batch data pipelines (Kafka, Spark, AWS ecosystem).
• Lead the evolution of a scalable lakehouse architecture for analytics and ML workloads.
• Design and productionize end-to-end ML pipelines, including feature engineering and real-time inference.
• Apply statistical methods (time-series, regression, anomaly detection) to high-impact business problems.
• Partner with stakeholders across operations, finance, and product to translate complex requirements into robust systems.
• Own infrastructure design using IaC (Terraform or equivalent) to ensure reproducibility and scale.
• Drive performance, reliability, and observability across data systems.
• Evaluate and introduce new tools and patterns across data engineering and applied AI.

Requirements

Tech Environment

• AWS (Glue, Lambda, ECS, S3, and related services)
• Apache Spark + lakehouse frameworks (Delta or equivalent)
• Kafka / streaming architectures
• Terraform (Infrastructure as Code)

What They’re Looking For

• Proven experience building and scaling data platforms in production environments.
• Deep expertise in streaming systems and distributed data processing.
• Strong system design skills with the ability to operate at both architecture and implementation levels.
• Solid grounding in statistical modelling and real-world applications.
• A bias toward ownership, pragmatism, and building systems that actually get used.