Technology - Brokerage Engineering - Senior Data Engineer

Kinvie; Toronto; Hybrid; CAD 120,000 - 143,000; Full time

Benefits offered by this job

Health & wellbeing resources and progr

Paid vacation, personal, and sick days

Competitive compensation and benefits

Hybrid environment with at least 3 in‑

Career growth and development

Community causes

Diverse, inclusive team

Job summary

QFG is hiring a Senior Data Engineer to own design, development, and operational excellence of scalable data pipelines and modern data platform infrastructure. You will work in a fast-paced, Agile environment across brokerage data, analytics, and governance initiatives.

The role requires 5+ years of data engineering experience, strong SQL/Python, and hands-on cloud data warehousing with GCP services. Hybrid work with in-office days and collaboration across product and engineering teams are

Qualifications

• Bachelor’s degree in CS, software engineering, information systems, or equivalent practical experience.
• 5+ years of progressive data engineering experience with production-grade pipelines.
• Advanced proficiency in SQL and Python for data engineering, automation, and transformations.
• Hands-on experience with distributed data processing using Google Cloud Dataproc and/or Apache Spark.
• Experience running data processing workloads on Kubernetes (containerization, scheduling, resource scaling).
• Hands-on experience with cloud data warehousing and orchestration platforms (GCP services such as BigQuery, Cloud Composer/Airflow, Dataflow, Pub/Sub, Databricks).
• Experience with dbt for data modeling, transformation, and automated testing.
• Experience with Terraform for IaC to provision and manage cloud infrastructure.
• Strong knowledge of relational and non-relational databases (MSSQL, PostgreSQL, CloudSQL).
• Experience with CI/CD pipelines, automated testing frameworks, and Git version control.

Responsibilities

• Lead design, development, and optimization of scalable batch and real-time data pipelines (ETL/ELT).
• Architect modular data models and dbt transformations with automated testing and lineage.
• Build and scale Spark workloads on Kubernetes for brokerage data.
• Provision and manage cloud data infrastructure using Terraform with CI/CD automation.
• Ensure data reliability, observability, and incident resolution across the stack.
• Enforce data governance, lineage, access control, and metadata cataloging.
• Collaborate with Product Managers, Solution Architects, and Engineers to deliver data products.
• Mentor junior engineers and enforce coding and deployment standards.

Skills

SQL

Python

Kubernetes

GCP

Spark

ETL/ELT

Data modeling

CI/CD

Data warehousing

Education

Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or equivalent

Tools

dbt

Terraform

BigQuery

Airflow

Dataproc

Pub/Sub

Databricks

Job description

What’s in it for you as an employee of QFG?

• Health & wellbeing resources and programs

• Paid vacation, personal, and sick days for work-life balance

• Competitive compensation and benefits packages

• Work-life balance in a hybrid environment with at least 3 days in office

• Career growth and development opportunities

• Opportunities to contribute to community causes

• Work with diverse team members in an inclusive and collaborative environment

This job posting is for an existing vacancy

We’re looking for our next Senior Data Engineer. Could It Be You?

As part of the Brokerage Engineering organization, the Senior Data Engineer is accountable for the technical design, development, and operational excellence of scalable data pipelines and modern data platform infrastructure. Operating in a high-velocity, agile delivery model, this role bridges business requirements, core trading and brokerage operations, and downstream analytics to deliver timely, reliable, and audit-compliant data products. By leveraging cloud data warehousing, automated transformations with dbt (Data Build Tool), distributed data processing with Spark on Kubernetes, GCP data services, and Infrastructure as Code, the role enhances platform performance and data self-service capabilities across the brokerage ecosystem.

Need more details? Keep reading…

In this role, responsibilities include but are not limited to:

• Pipeline Architecture & Engineering: Lead the technical design, development, and optimization of scalable, high-performance batch and real-time streaming data pipelines (ETL/ELT), ensuring robust data ingestion, orchestration, compute efficiency, and timely availability for critical brokerage trading and digital investing platforms.

• Data Modeling & Transformation with dbt: Architect, implement, and maintain modular, version-controlled data models and transformation workflows using SQL and dbt, establishing robust automated testing, documentation, and data lineage to deliver analytics-ready and audit-compliant data assets.

• Distributed Data Processing & Spark Orchestration: Build, tune, and scale high-throughput big data processing workloads utilizing Google Cloud Dataproc and Apache Spark running on Kubernetes, optimizing cluster resource allocation, memory management, and job execution for complex brokerage datasets.

• Infrastructure as Code (IaC) & Cloud Provisioning: Provision, configure, and manage resilient cloud data infrastructure across environments using Terraform, embedding CI/CD automation, security best practices, and cost-effective resource management into all data platform deployments.

• Data Reliability, Observability & Incident Resolution: Analyze and resolve complex technical problems and production data anomalies across the stack, diagnosing root causes through systematic investigation and implementing durable monitoring, automated alerting, and self-healing solutions to prevent recurrence.

• Data Governance, Lineage & Regulatory Integrity: Ensure comprehensive data quality, data lineage, role-based access control, and metadata cataloging across all brokerage data pipelines to satisfy strict financial industry governance, auditability, and regulatory reporting standards.

• Cross-Functional Solution Delivery: Partner closely with Product Managers, Solution Architects, Software Engineers, and Data Science/ML Ops teams to translate complex business requirements and predictive models into robust, production-grade data products and integration workflows.

• Standards, Quality & Technical Mentorship: Define and enforce coding, testing, and deployment standards across data repositories, conducting thorough code reviews and providing technical guidance and mentorship to junior and intermediate engineers to foster engineering excellence.

So are YOU our next Senior Data Engineer ? You are if you…

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or equivalent practical experience.

• 5+ years of progressive data engineering experience with demonstrated success delivering robust, production-grade data pipelines.

• Advanced proficiency in SQL and Python for data engineering, automation, and complex data transformations.

• Hands-on production experience with distributed data processing using Google Cloud Dataproc and/or Apache Spark.

• Demonstrated hands-on experience running and orchestrating data processing workloads on Kubernetes (containerization, pod scheduling, and resource scaling).

• Hands-on production experience with cloud data warehousing and orchestration platforms (e.g., Google Cloud Platform services such as BigQuery, Cloud Composer/Airflow, Dataflow, Pub/Sub, or Databricks).

• Demonstrated working knowledge and hands-on experience using dbt (data build tool) for data modeling, transformation, and automated testing.

• Demonstrated working knowledge of Infrastructure as Code (IaC) utilizing Terraform to provision, configure, and maintain cloud infrastructure.

• Strong experience with relational and non-relational database management systems (e.g., MSSQL, PostgreSQL, CloudSQL).

• Demonstrated experience with modern CI/CD deployment pipelines, automated testing frameworks, and version control (Git).

Additional kudos if you…

• Direct experience within financial services, brokerage technology, trading systems, capital markets, or wealth management data domains.

• Experience with event-driven or real-time streaming architectures (e.g., Apache Kafka, Google Cloud Pub/Sub).

• Relevant cloud or engineering certifications (e.g., Google Cloud Certified Professional Data Engineer, Databricks Certified Data Engineer, or Certified Kubernetes Administrator/Application Developer).

• Experience working within Agile/Scrum or Scaled Agile Framework (SAFe) environments.

• Practical experience leveraging AI-assisted developer tooling (e.g., Claude, GitHub Copilot, Cursor) to optimize development velocity and code quality.

Compensation Information:

• Base salary range: $120,000 - $143,000

• The final compensation package will be commensurate with the successful candidate's experience, skills, and geographic location (Canada). It includes a comprehensive benefits plan and a competitive incentive (bonus) program for Full-Time Permanent roles.