Vanguard

Manages investment funds and retirement savings

Sr Data Engineer - Specialist

Data EngineeringConfirmed live in the last 24 hours

Full-timeToronto, ON, Canada

About the Job

Requirements

At least 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, using services for data storage, ingestion, pipeline orchestration, database or lakehouse management, and data transformation.
At least 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, including dimensional, canonical, data vault, or domain-driven approaches.
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.

Responsibilities

Design, build, and optimize scalable, secure, and repeatable data pipelines using AWS services including S3, Glue, Lambda, Step Functions, Redshift, and IAM, and Databricks technologies including PySpark, Lakeflow, Delta Lake, and Unity Catalog.
Serve as the technical leader for data ingestion pipelines and model new datasets, including Advisor360, CRM, ETF, and Mutual Fund platforms.
Apply data modeling principles, including dimensional, canonical, and domain-driven approaches, to support analytics, reporting, and AI/ML use cases.
Ensure alignment with enterprise data architecture standards and promote reusability, governance, and long-term maintainability.
Independently investigate data issues and propose solutions that balance performance, cost, risk, and business needs, with limited guidance.
Engage business stakeholders to gather ambiguous requirements, ask clarifying questions, and translate requirements into clear technical designs.
Recommend technical patterns, frameworks, and toolsets.
Implement data quality frameworks, monitoring, and alerting for business-critical data assets.
Troubleshoot data inconsistencies and ensure appropriate logging, testing, and recovery mechanisms across pipelines.
Lead regression testing, software upgrades, and production deployments with change control discipline.
Lead all phases of solution development, from design through deployment and operationalization.
Mentor and guide 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 improve data engineering maturity across the organization.
Participate in strategic initiatives such as AI readiness, data unification, metadata strategy, and enterprise integration roadmaps.
Drive continuous improvement in engineering frameworks, onboarding workflows, and platform capability.

Desired Qualifications

Experience in asset management, wealth management, or financial services, including ETF, Mutual Fund, CRM, or 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 a related field.

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 robustdata 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 lakehouse management,data transformation.

5+ years of hands-on experience designing, developing, and supporting enterprise-scale data pipelines on the Databricks Lakehouse platform usingPySpark, Delta Lake, Databricks Workflows, andLakeflowDeclarative 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.

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. Your recruiter will be able to provide additional details on the total compensation offering during the hiring process.

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 [email protected] , 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.

About the Company

Vanguard

Manages investment funds and retirement savings

Vanguard manages mutual funds, exchange-traded funds, and retirement investments for individuals, financial advisers, and institutions. Its Vanguard 500 Index Fund gives investors exposure to large U.S. companies by tracking the S&P 500, while its lineup also includes actively managed stock and bond funds. Clients invest through brokerage and retirement accounts, workplace plans, or advisers, and advisory services build and maintain portfolios around their goals. Fund expenses and advisory fees pay for investment management and account services, with advisory charges based on the assets managed. Founded in 1975, Vanguard is headquartered in Malvern, Pennsylvania, United States.

Company size: 10,001+
Company stage: Debt Financing
Total funding: $4B
Headquarters: Malvern, PA
Founded: 1975

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