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Added 3 days ago
R
Senior Platform Automation Engineer
RBC
Toronto, ON
- Full-time
Develop Python automation frameworks to streamline observability deployments, validation, and configuration management across environments.
Added 3 days ago
Bilingual Senior Specialist, Software Engineering (CI/CD & Automation Engineering)
CMHC - SCHL
Ottawa, ON · Montreal, QC
Proficiency in scripting and automation using PowerShell, Bash, or Python.
Added 3 days ago
PG
Senior Application Security Engineer
Paymentus Group Inc
Richmond Hill, ON
- Full-time
The Senior Application Security Engineer is responsible for helping secure the Paymentus SaaS platform by partnering directly with software engineering, product, cloud infrastructure,…
Added 3 days ago
AI Security Engineer, AWS Security - AISec
Amazon
Vancouver, BC · NS
As an AI Security Engineer, you will work with software development teams to ensure the security of AI applications. You will drive security strategy and influence product security…
Added 3 days ago
AM
IP Silicon Validation Engineer
Advanced Micro Devices, Inc
Vancouver, BC
- Full-time
The AMD NBIO Team is on the lookout for a dynamic, upbeat IP Validation Design Engineer to join our growing team. As a key contributor to the success of AMD’s IP, you will be part of a…
Added 3 days ago
ES
Senior Data Engineer
Exchange Solutions
Toronto, ON · Greater Toronto Area, Canada
- Full-time
Write advanced SQL and Python to develop transformations, frameworks, automation, and reusable engineering tooling.
Added 3 days ago
T
Senior Python Test Automation Engineer
Telesat
Ottawa, ON
- Full-time
We are seeking a senior Python Automation Engineer to design, build, and maintain scalable test automation frameworks for distributed, event-driven applications. This is a hands-on…
Added 3 days ago
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AWS DevOps Engineer / Toronto, ON (Onsite) - FTE
AceStack
Toronto, ON
- Full-time
We are seeking an experienced AWS DevOps Engineer to design, implement, and maintain scalable, secure, and highly available cloud infrastructure. The ideal candidate will have strong…
Added 3 days ago
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Data Integration Engineer
Prophix
Toronto, ON · Etobicoke, ON
- Full-time
…Financial Performance Platform. We bring planning, reporting, and automation together so people can focus on higher-value work instead of repetitive tasks. As we introduce more AI-enabled…
Added 3 days ago
NB
Senior Data Engineer
National Bank of Canada
Montreal, QC
- Full-time
Integrate and automate data between systems and business applications, then optimise processing with SQL and Python.
Added 3 days ago
MT
CAD Engineer (Conformal LEC)
Mirafra Technologies
Canada
- Full-time
Automation: Write and maintain scripts and automation infrastructure using Tcl, Python, Perl, or Shell.
Added 3 days ago
JC
Senior Machine Learning Engineer - (Biometrics)
Jumio Corporation
Montreal, QC
- Full-time
Machine Learning Engineer – Biometrics (Computer Vision)
Added 3 days ago
AI
Quality Engineer (QE) / SDET – AI Chatbot Validation
Apptoza Inc.
Toronto, ON
- Contract
Design, develop, and maintain scalable test automation frameworks using Java, Python, or Groovy.
Added 3 days ago
HB
Trading Application Support Engineer: $180,000cad + Bonus (Quant Fund)
Hunter Bond
Montreal, QC
- Full-time
Solid Python scripting skills for automation and tooling
Added 3 days ago
UT
ASIC/RTL Design Engineer 5 # 26-27793
US Tech Solutions
Markham, ON
- $78 to $83 CAD / hour
- Contract
Proficiency in scripting languages (TCL, Perl, Python, or Shell) for flow automation.
Added 3 days ago
JG
DevOps Engineer - CI/CD & AUTOMATION - 8+ Years
J2B Global LLC
Mississauga, ON
- Full-time
Job Summary: We are seeking a self-reliant DevOps Engineer to work closely with software developers, security teams, and system administrators to build and maintain scalable, resilient, and…
Added 3 days ago
HB
Platform Engineer - $250,000+ bonus! (Fintech)
Hunter Bond
Montreal, QC
- Full-time
Automation: infrastructure automation, configuration management and self-service enablement
Added 3 days ago
SS
Software Safety Engineer – AI, LLM & SW-FMEA Automation
Sopra Steria
Montreal, QC
- Full-time
We are seeking a Software Safety Engineer with expertise in Artificial Intelligence (AI), Large Language Models (LLMs), and software safety engineering to support an initiative focused on…
Added 3 days ago
SS
Embedded Software Test Engineer / HSIT – ISO 26262
Sopra Steria
Ottawa, ON
- Full-time
We are looking for a detail-oriented Embedded Software Test / Verification & Validation Engineer to support testing activities within an HSIT team in an automotive environment.
Added 3 days ago
Manager, Quantitative Strategies & Analytics
Scotiabank
Toronto, ON
…investment decision‑making, drive innovation, and support process…
Added 3 days ago
Manager, Quantitative Strategies & Analytics
Scotiabank
Toronto, ON
…investment decision‑making, drive innovation, and support process…
Added 3 days ago
AI
Quality Engineer (QE) / SDET – AI Chatbot Validation
Apptoza Inc.
Toronto, ON
- Contract
Python, Selenium, Playwright, PyTest, or similar automation tools.
Added 3 days ago
SG
Senior DevOPS Engineer
Societe Generale
Montreal, QC
- Full-time
Drive adoption of DevOps best practices, automation, and software craftsmanship
Added 3 days ago
S
DevOps Engineer
Synechron
Montreal, QC
- $80,000 to $90,000 CAD / year
- Full-time
…that supports fast and secure software delivery. The DevOps Engineer will manage CI/CD pipelines, cloud platforms, monitoring, and deployment processes while minimizing downtime. This role…
Added 3 days ago
HB
Senior Linux Platform Engineer - Trading Firm - $150,000-$350,000 CAD + Bonus
Hunter Bond
Montreal, QC
- $150,000 to $350,000 CAD / year
- Full-time
Skills: Linux, Python, Ansible, Chef, Storage, Observability, Cloud

Exchange Solutions

Senior Data Engineer

Toronto, ON, Canada
Greater Toronto Area, Canada, Canada

Full-time

Apply About employer

Applications open in a new tab.

Job details

Job type: Full-time
Apply by: Dec 3, 2026
Experience: A minimum of 5 years’ progressive data engineering experience, with a demonstrated record of owning production data pipelines end-to-end.
Posting language: English
Posted: Nov 3, 2026

Original job description

Role Summary

The Senior Data Engineer reports to the Director, Information Technology Operations and designs, builds, and scales the data systems that support analytics, AI-enabled experiences, and organizational decision-making. The role owns significant parts of the data platform, including production pipelines, transformation layers, data models, semantic layers, data quality, observability, and engineering standards. It provides technical direction, mentors engineers and analysts, and partners with technical and business teams to deliver trusted, well-modelled data products. The role also evaluates and implements AI-assisted data engineering capabilities to improve pipeline development, transformation, documentation, metadata, data quality, and governed access to enterprise data while maintaining appropriate engineering controls and review.

Primary Responsibilities:

Data Platform Architecture & Technical Direction

Responsible for key contribution of significant parts of the data platform and drive long-term architectural decisions related to tooling, data modelling, semantic architecture, automation, and scalability.
Design data platform capabilities that reliably support analytics, reporting, operational use cases, and AI-enabled experiences.
Evaluate technical options and establish reusable patterns that improve platform maintainability, extensibility, and engineering effectiveness.

Pipeline Development & Transformation

Design, build, and maintain scalable, reliable production data pipelines and transformation layers using dbt and a medallion Bronze, Silver, and Gold architecture.
Write advanced SQL and Python to develop transformations, frameworks, automation, and reusable engineering tooling.
Responsible for production data pipelines end-to-end, including design, development, testing, deployment, monitoring, troubleshooting, optimization, and ongoing support.

Data Modelling & Semantic Architecture

Lead data modelling across dimensional models, slowly changing dimensions, fact and dimension design, and well-structured Silver and Gold layers.
Design and evolve semantic layers that define business entities, metrics, relationships, calculations, and governed business logic for consistent use across BI, analytics, and AI applications.
Develop metadata-driven and governed data structures that make enterprise data discoverable, understandable, and consistently consumable.

AI-Assisted Data Engineering & Enablement

Evaluate and apply AI-assisted engineering approaches to accelerate pipeline design, transformation logic, SQL generation, testing, documentation, troubleshooting, and optimization while maintaining strong review and engineering controls.
Explore AI-assisted schema understanding, metadata generation, relationship discovery, business-concept mapping, and generation of semantic definitions.
Partner with Analytics and Innovation AI Labs to enable natural-language-to-data, intelligent analytics, semantic retrieval, and other AI or LLM-based uses of governed enterprise data.

Data Quality & Observability

Responsible for evolving key elements of the data quality strategy, including testing frameworks, monitoring, alerting, service levels, observability, anomaly detection, and platform-wide validation.
Implement controls that identify pipeline, transformation, model, metadata, and semantic-layer issues early and support timely root-cause analysis and remediation.
Promote trusted data products through clear quality standards, transparent monitoring, and continuous improvement of reliability and usability.

Engineering Practices & Delivery Standards

Establish and champion engineering practices covering version control, CI/CD, code review, documentation, automated testing, clean maintainable code, and dbt project standards.
Develop reusable frameworks and automation that improve delivery consistency, reduce manual effort, and support safe changes across data environments.
Contribute to governance, cataloguing, lineage, metadata management, and data-contract practices that strengthen understanding and control of enterprise data.

Partnership, Mentorship & Team Growth

Collaborate with analysts, data scientists, product teams, and business stakeholders to translate business requirements into trusted, well-modelled data products.
Mentor and support engineers and analysts, provide technical guidance, and contribute to team capability and engineering maturity.
Communicate architecture decisions, data definitions, quality expectations, risks, and technical trade-offs clearly to technical and business stakeholders.
Contribute to the culture of the company, fostering Exchange Solutions' core values and policies.

Capability Requirements - Education, Skills & Experience:

Education & Experience

Post secondary education, with a degree in Data Engineering.
A minimum of 5 years’ progressive data engineering experience, with a demonstrated record of owning production data pipelines end-to-end.
Experience designing, developing, and operating data platforms that support analytics, reporting, operational use cases, or AI-enabled applications.
Experience translating business and analytical requirements into trusted data products in collaboration with technical and business stakeholders.
Experience leading data engineering projects or mentoring engineers and analysts is an asset.
Experience in a loyalty, retail, financial services, or other data-intensive environment is an asset.

Technical Skills

Expert SQL skills and strong Python proficiency for data engineering; working knowledge of Jinja.
Deep hands-on dbt experience, including advanced modelling, macros, packages, testing, documentation, and project architecture using dbt Core or dbt Cloud.
Strong command of medallion architecture and data modelling, including dimensional modelling, slowly changing dimensions, fact and dimension design, and semantic layers.
Experience designing or working with semantic models, governed business metrics, metadata-driven architectures, or governed business definitions.
Experience with cloud data warehouses such as Snowflake, BigQuery, Databricks, or Redshift and with cloud platforms such as AWS, Azure, or GCP.
Experience building and operating data quality, testing, monitoring, alerting, lineage, cataloguing, or observability capabilities.
Solid software engineering practices covering Git, CI/CD, automated testing, code review, documentation, and clean maintainable code.
Experience with orchestration tools such as Airflow, Dagster, Prefect, dbt Cloud, or Snowflake Tasks is an asset.
Familiarity with streaming or real-time data and infrastructure-as-code tools such as Terraform is an asset.
Ability to evaluate and safely incorporate AI-assisted tools into data and software engineering workflows, including development assistance, metadata enrichment, automated documentation, and intelligent data-quality tooling.
Experience with AI or LLM-enabled data platforms, natural-language-to-SQL, semantic retrieval, knowledge graphs, ontologies, semantic views, or agentic data engineering workflows is an asset.

Competencies & Behaviours

Ownership & Accountability - Takes end-to-end responsibility for production data pipelines, platform components, engineering quality, and resolution of risks or failures.
Architecture & Technical Judgement - Makes practical, forward-looking decisions that balance scalability, reliability, maintainability, data quality, delivery needs, and long-term platform direction.
Communication - Translates business requirements and complex data concepts into clear models, technical decisions, documentation, and guidance for technical and non-technical stakeholders.
Collaboration & Mentorship - Builds effective relationships across engineering, analytics, data science, Product, and business teams and raises team capability through practical coaching and knowledge sharing.
Innovation & Responsible AI - Explores new tools and AI-assisted approaches thoughtfully, validates their value, and applies appropriate review, governance, quality, and security controls.
Attention to Detail - Applies a disciplined approach to modelling, transformation logic, testing, metadata, semantic definitions, documentation, and production changes.

Apply About employer

More about this job

Responsibilities

Design, build, and operate scalable data platform components, production pipelines, transformation layers, data models, and semantic layers that support analytics, reporting, and AI-enabled applications. Set engineering standards, improve data quality and observability, evaluate responsible AI-assisted engineering approaches, and provide technical direction and mentorship while partnering with business and technical teams.

Requirements

Requires post-secondary education with a degree in Data Engineering and at least five years of progressive data engineering experience, including end-to-end ownership of production pipelines. Candidates should have expert SQL, strong Python, deep dbt experience, data modeling and cloud data platform expertise, and familiarity with engineering practices, data quality, and responsible AI-assisted tools.

Skills

SQL
Python
dbt
Jinja
Data Pipeline Engineering
Medallion Architecture
Dimensional Modeling
Semantic Layers
Cloud Data Warehousing
Data Quality and Observability
CI/CD
Git
Data Governance
AI-Assisted Engineering
Mentorship

Visa sponsorship

Not detected in the job text

Keywords

SQL
Python
Jinja
dbt Core
dbt Cloud
Medallion Architecture
Bronze-Silver-Gold Architecture
Dimensional Modeling
Slowly Changing Dimensions
Semantic Layers
Snowflake
BigQuery
Databricks
Redshift
AWS
Azure
GCP
Airflow
Dagster
Prefect
Snowflake Tasks
Terraform
CI/CD
Git
Data Quality
Data Observability
Data Governance
Metadata Management
Natural-Language-to-SQL
LLMs

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