Database Engineer - HR à la carte

Position

Database Engineer

Location

Toronto, ON (100% onsite)

Employment Type

New, Full-Time Permanent position

Salary

$110,000 - $125,000 (depending on experience) plus incentive compensation

Hamilton ETFs is building a SaaS platform-a suite of applications designed to power every aspect of our business, including sales, marketing, data intelligence, compliance, reporting, and AI-assisted workflows. Purpose-built for the unique needs of a Canadian asset manager, this platform will be the foundation for how we operate and innovate.

This is not a maintenance-only Database Engineer role. You'll have the opportunity to design and build a modern, cloud-native, ML-ready data platform that supports transactional workloads, analytics, regulatory reporting, and the next generation of AI capabilities, including vector search and semantic retrieval.

If you're excited by the opportunity to architect a platform from the ground up and shape the future of data and AI at a growing investment firm, we'd love to hear from you.

Hamilton ETFs offers:

• A competitive compensation package including comprehensive health and wellness benefits;
• Ownership of the data foundation for a platform at its earliest, most formative stage;
• A direct line into technology strategy at a growing Canadian asset manager;
• We value collaboration, open-mindedness and service focused mindsets;
• Diverse and inspiring colleagues and approachable leaders;
• Opportunity to evolve in a growing firm.

Hamilton ETFs is one of Canada’s fastest growing exchange traded fund (“ETF”) providers. Based in Toronto’s financial district and with ~$17 billion in ETF assets under management, we are seeking a motivated individual to join our entrepreneurial team of experienced and talented professionals.

Here’s what you will do as Database Engineer:

The Database Engineer is responsible for designing and evolving the shared data platform that powers Hamilton ETFs' next-generation SaaS applications. You'll architect secure, scalable, and high-performing databases that support advisor intelligence, CRM workflows, compliance monitoring, reporting, analytics, and future AI capabilities.

• Design and manage the core relational database architecture that serves as the shared foundation for the platform's apps.
• Design scalable data models supporting advisor intelligence, CRM, marketing, compliance, reporting, and customer-facing applications.
• Own database performance: indexing, partitioning, query optimization, replication, backups, recovery, and high availability.
• Establish robust data governance practices, including role-based access control, audit logging, encryption, data lineage, and retention policies aligned with Canadian regulatory requirements.
• Support secure ingestion, normalization, and deduplication of large financial-industry datasets, including the Canadian advisor database.
• Design structures that support ML features such as lead scoring, intent detection, advisor similarity, anomaly detection, and natural-language reporting.
• Work with the Full Stack Developer to define API-ready patterns, stored procedures where appropriate, and efficient service-layer access.
• Ensure clean, governed data flows into HubSpot as the commercial source of truth.
• Help design future infrastructure: data warehouse, event streaming, vector database, semantic search, and analytics layers.
• Document standards, naming conventions, backup procedures, security controls, and data lifecycle policies.
• Other work or special projects assigned for the overall benefit of Hamilton ETFs.

• Strong understanding that the database is the foundation of the product, and passionate about the performance, security, clean data models, maintainability, and future flexibility.
• Has comfort in an early-stage environment (where the architecture is being built from the start) and is energized by creating a strong foundation.
• Ability to work closely with application developers and product leadership, with strong documentation and operational discipline.
• Architectural mindset with the ability to design systems that will scale as products, data volumes, and AI capabilities evolve.

• Post secondary diploma in computer science, computer engineering or other relevant programs; Masters Degree an asset.
• 5+ years designing and supporting relational databases as a Database Engineer, Data Engineer, Database Developer, or similar role.
• Strong PostgreSQL experience: performance tuning, indexing strategies, schema design, and database security.
• Experience designing databases for SaaS, CRM, workflow, reporting, or financial services platforms.
• Strong grasp of data privacy, auditability, role-based access control, encryption, backup, and disaster recovery.
• Experience with ETL/ELT pipelines, data imports, deduplication, validation, and large structured datasets.

Preferred Education, Experience and Skills

• PostgreSQL at scale: partitioning, replication, read replicas, materialized views, and query profiling.
• Cloud platforms such as AWS, Azure, or GCP.
• Data warehouse or analytics tooling.
• Vector databases such as pgvector, Qdrant, Pinecone, Weaviate, or similar.
• Supporting ML workflows, semantic search, embeddings, or LLM-backed applications.
• Financial services, wealth management, asset management, capital markets, or regulated environments.
• Familiarity with SOC 2, ISO 27001, audit controls, data retention, and compliance reporting.
• Event-driven architectures, message queues, or streaming pipelines.

Hamilton ETFs is committed to meeting the accessibility needs of all applicants throughout the recruiting and selection process. Please let us know about any accommodation and/or support requirements. Please note only those candidates selected for an interview will be contacted.

Hamilton ETFs is working in partnership with HR à la carte for our recruitment efforts.

Please note: we are only accepting applications from those authorized to work in Canada and we are not currently sponsoring any temporary or permanent work visas.

Use of AI in Hiring: Some stages of our recruitment process may use AI-assisted tools to support recruitment efforts; however, all applications are screened and assessed by human reviewers. In addition, all final hiring decisions are made by humans.