Technology - Brokerage Engineering - Senior Data Engineer

Flexiti Financial; Toronto; Hybrid; CAD 171,000 - 204,000; Full time

Benefits offered by this job

Health & wellbeing resources

Paid time off (vacation, personal, and

Paid vacation, personal, and sick days

Hybrid work environment

Competitive compensation

Career growth opportunities

Community involvement

Inclusive environment

Job summary

QFG is seeking a Senior Data Engineer to lead the design, development, and operation of scalable data pipelines for its fast-paced brokerage platforms. You will leverage cloud data warehousing, dbt transformations, Spark on Kubernetes, and Terraform IaC to deliver reliable, auditable data assets and self-service analytics.

The ideal candidate has 5+ years of experience with SQL and Python and hands-on cloud experience.

Qualifications

• 5+ years of progressive data engineering experience delivering robust pipelines.
• Advanced proficiency in SQL and Python for data engineering.
• Hands-on experience with distributed data processing on Kubernetes.
• Experience with cloud data warehousing and orchestration platforms (GCP).
• Experience with dbt for modeling and testing.
• IaC using Terraform in cloud deployments.
• Experience with relational and non-relational databases.
• Experience with CI/CD pipelines and version control (Git).

Responsibilities

• Lead design, development, and optimization of scalable ETL/ELT pipelines for trading platforms.
• Architect data models and transformation workflows using SQL and dbt.
• Build and scale Spark workloads on Kubernetes for brokerage datasets.
• Provision and manage cloud data infrastructure with Terraform and CI/CD automation.
• Analyze data reliability, observability, and resolve production anomalies.
• Ensure data governance, lineage, and regulatory compliance.
• Collaborate with product managers and engineers to deliver robust data products.
• Enforce coding, testing, and deployment standards and mentor junior engineers.

Skills

SQL

Python

dbt

Google Cloud Dataproc

Apache Spark

Kubernetes

Terraform

BigQuery

Data Warehousing

CI/CD

Git

Data Modeling

Data Governance

Education

Bachelor's degree in Computer Science, Software Engineering, Information Systems, or equivalent practical experience

Tools

Google Cloud Dataproc

Kubernetes

dbt

Terraform

BigQuery

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.