Sr Data Engineer, Specialist

The Vanguard Group; Toronto; Hybrid; CAD 124,000 - 193,000; Full time

Benefits offered by this job

Health insurance

Accident and life insurance

Retirement savings plans

Job summary

Vanguard is seeking a Sr Data Engineer to architect and build scalable data pipelines on AWS and Databricks. You will model datasets for analytics, CRM integrations, and fund data while promoting best practices and governance.

You will act as a technical consultant, collaborate with stakeholders, and mentor teammates to raise engineering standards. This role is hybrid and based in Canada, with a focus on enterprise data platforms.

Qualifications

• 5+ years in data engineering or large-scale data systems.
• Expert AWS, GCP, or Azure experience for data platforms.
• 5+ years on Databricks with PySpark, Delta Lake, and workflows.
• Strong data modeling: dimensional, canonical, or domain-driven.
• Ability to translate complex problems into scalable designs.

Responsibilities

• Design, build, and optimize scalable data pipelines on AWS and Databricks.
• Lead data ingestion for datasets like Advisor360, CRM, ETF, and funds.
• Ensure data quality, testing, and robust monitoring across pipelines.
• Mentor engineers and promote best practices in architecture and tooling.
• Collaborate with stakeholders to deliver analytics-ready datasets.

Skills

AWS

Databricks

Python

SQL

Data modeling

ETL/ELT

CI/CD

GitHub

Education

Undergraduate degree or equivalent

Tools

Databricks

dbt

Lakehouse/Delta Lake

Job description

Job Overview

We are looking for a Sr Data Engineer with in-depth expertise in AWS, Databricks, and modern data architecture and data modeling to help build the next generation of our data foundation , including data platforms such as Advisor360, CRM integrations, ETF/MF data pipelines, and other enterprise analytics assets.

This is a senior, highly autonomous role. You will operate as a technical consultant and data architecture thinker, working closely with stakeholders to understand ambiguous business needs, conduct independent investigations, and design solutions that are scalable, reliable, and aligned to both business and technology goals. Ideal candidates have 5+ years of data engineering experience and thrive in environments where they drive clarity, define standards, and elevate engineering best practices across the team.

Responsibilities

• Data Engineering & Architecture: Design, build, and optimize scalable, secure, and repeatable data pipelines using AWS (S3, Glue, Lambda, Step Functions, Redshift, IAM) and Databricks (PySpark, Lakeflow, Delta Lake, Unity Catalog). Serve as the technical leader for data ingestion pipelines, modeling new datasets such as Advisor360, CRM, ETF, and Mutual Fund platforms. Apply strong data modeling (dimensional, canonical, and domain-driven) principles to support analytics, reporting, and AI/ML use cases. Ensure alignment with enterprise data architecture standards, promoting reusability, governance, and long‑term maintainability.
• Consultative Problem Solving: With limited guidance, independently perform deep investigations, identify data issues, and propose solutions that balance performance, cost, risk, and business needs. Engage business stakeholders to gather ambiguous requirements, ask the right questions, and translate them into clear technical designs. Provide thought leadership and recommend technical patterns, frameworks, and toolsets.
• Data Quality, Reliability & Operations: Implement robust data quality frameworks, monitoring, and alerting to ensure high trust in business-critical data assets. Troubleshoot data inconsistencies and ensure proper logging, testing, and recovery mechanisms across pipelines. Lead regression testing, software upgrades, and production deployments with strong change control discipline.
• Collaboration & Leadership: Lead all phases of solution development—from design to deployment and operationalization. Mentor and guide other engineers in coding standards, architecture patterns, Databricks best practices, and AWS platform usage. Partner with Data Architecture, Analytics, Product, and Business teams to deliver solutions that improve decision‑making. Provide training sessions and documentation to uplift the data engineering maturity across the organization.
• Special Projects: Participate in strategic initiatives such as AI readiness, data unification efforts, metadata strategy, and enterprise integration roadmaps. Drive continuous improvement in engineering frameworks, onboarding workflows, and platform capability.

Required Skills

• 5+ years of experience in data engineering, data architecture, or large‑scale distributed data systems.
• Expert‑level experience with cloud platforms such as AWS, GCP, or Azure, leveraging services for data storage, ingestion, pipeline orchestration, database or lake house management, data transformation.
• 5+ years of hands‑on experience designing, developing, and supporting enterprise‑scale data pipelines on the Databricks Lakehouse platform using PySpark, Delta Lake, Databricks Workflows, and Lakeflow Declarative Pipelines.
• Strong background in data modeling (dimensional, canonical, data vault, or domain‑driven).
• Proven ability to work independently with minimal direction and deliver high‑quality solutions in ambiguous environments.
• Demonstrated experience translating complex business problems into scalable technical solutions.
• Strong SQL and Python skills, with emphasis on ETL/ELT pipeline development.
• Experience with CI/CD, GitHub, DevOps workflows, and automated testing.

Preferred Skills

• Experience in asset management, wealth management, or financial services (ETF, Mutual Funds, CRM, Advisor analytics).
• Experience with enterprise data quality tools and metadata management concepts.
• Familiarity with modern semantic layers, dbt, or domain‑oriented data mesh concepts.
• Undergraduate degree or equivalent professional experience in Computer Science, Engineering, Information Systems, or related field.

Compensation

Pay Transparency Expected Salary Range: $90,000-$140,000. Our compensation ranges are based on role, level, and local market. Individual pay within the range is determined by factors such as job‑related skills, experience, and relevant education or training. For part‑time roles, pay will be pro‑rated based on regularly scheduled hours.

In addition to the range above, Vanguard’s total compensation package may include performance‑based incentives, discretionary bonuses, and other perks. We also offer comprehensive benefits such as health insurance, accident and life insurance, and retirement savings plans.

• Health insurance
• Accident and life insurance
• Retirement savings plans

AI Disclosure Statement

Vanguard does not use artificial intelligence (AI) or automated decision‑making tools in its recruitment process. All application reviews and hiring decisions are made by our recruitment team.

Accommodations

Vanguard is committed to fostering an accessible and inclusive workplace. We are committed to providing barrier‑free and accessible employment practices in compliance with the Accessibility for Ontarians with Disabilities Act (AODA) and the Ontario Human Rights Code. If you require accommodation at any stage of the recruitment process, please inform us. We will work with you to meet your needs, and you can reach us at [e-mail] , quoting the job ID and job title.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in‑person learning, collaboration, and connection. We believe our mission‑driven and highly collaborative culture is a critical enabler to support long‑term client outcomes and enrich the employee experience.

Vanguard, one of the world's leading investment management companies, serves individual investors, institutions, employer‑sponsored retirement plans, and financial professionals. We have a diverse and talented crew with a culture that promotes teamwork, along with an unwavering focus on serving our clients' best interests.