Software Engineer III

Description

This is a lead software engineer role that owns complete solutions for large enterprise platforms and complex initiatives within one solution area, while also influencing neighbouring domains.

You set technical direction and define and govern architecture, working hands-on across varied cloud environments to design solutions that scale securely and reliably.

The role sits in the Enterprise Data Management Office (EDMO), where the team delivers Data Catalog services as a product for company-wide data discovery, governance and self-service access to trusted data.

You act as a primary subject matter expert across several technical areas, work independently as a senior lead on complex tasks and operational support, anticipate gaps, and turn emerging industry trends into practical engineering direction. You generally report to a Senior Manager, Engineering.

The company is a global financial institution, the fifth largest bank in North America by branches, serving more than 27 million households and businesses in Canada, the United States and abroad, with over 95,000 colleagues.

Responsibilities

• Keep architecture decisions documented, traceable and aligned with enterprise standards, with trade-offs stated and risks managed.
• Lead delivery of end-to-end technical solutions from design through build, test, deployment and operation, with strict SDLC discipline.
• Build cloud architecture and infrastructure that grows in a scalable, resilient, secure and cost-effective way across different deployment environments.
• Keep production stable with high availability, defined SLOs and SLAs, fast incident recovery and useful observability.
• Write critical path components yourself, raise engineering standards, and grow the team through mentoring, shared patterns and automation.
• Work with Architecture, Security, Infrastructure and Product so solutions are resilient, secure by design, scalable, observable and compliant with enterprise standards.
• Define solution options, align cross-functional stakeholders, own execution and make sure complex systems are ready for production.
• Deliver critical components, clear bottlenecks, and move to a target architecture through incremental, low-risk migration.
• Define and validate non-functional requirements, automate delivery, manage dependencies, and keep improving performance, availability and cost efficiency.
• Choose scalable infrastructure patterns, deployment models and integration approaches across cloud environments that meet enterprise standards.
• Promote AI-assisted development with copilots and LLM agents while keeping enterprise-grade standards.
• Create AI guardrails covering approved use cases, prompt patterns, code provenance expectations and documentation standards.
• Build validation pipelines for AI-generated code, including linting and formatting, unit and integration tests, SAST and DAST, dependency scanning, secret detection and architecture rules.
• Set human review rules: code review checklists, threat modelling and design reviews for high-risk changes.
• Set up a repeatable AI workflow for the team covering task breakdown, generation, verification, refactoring and documentation, and measure its effect on cycle time.
• Encourage responsible use across privacy, data handling, IP, auditability and compliance with internal policies and external regulations.
• Keep governance, compliance and risk aligned with enterprise frameworks, drive approvals, and manage policies according to business priorities and risk appetite.
• Enforce shift-left quality, lead design and code reviews, and oversee release readiness and operational gating.
• Lead enterprise-level performance analysis, drive remediation and continuous improvement, reduce costs through FinOps practices, and track emerging trends and regulatory needs.
• Create reusable patterns, reference implementations, documentation and coaching so the team relies on shared approaches rather than individual heroics.
• Give hands-on technical guidance, unblock complex problems, and share knowledge through RFCs, guilds and reviews.
• Help build an inclusive, high-performing team by supporting hiring, onboarding and equitable practices, and by raising risks early.

Requirements

• Strong problem-solving skills, with a record of diagnosing complex system failures and designing resilient solutions.
• Hands-on experience designing normalized, extensible relational data models, including schemas, entities, relationships, keys, constraints, versioned history, and models that accept multiple source structures.
• Hands-on experience designing and building knowledge graph or graph data models: nodes, edges, relationship meaning, traversal and query patterns, and graph capabilities on PostgreSQL or graph databases for connected discovery, lineage, dependency mapping and impact analysis.
• Strong hands-on experience with PostgreSQL, SQL, Python and data engineering, including schemas, indexing, query optimization, JSON/JSONB where suitable, transformations, change tracking and high-volume processing.
• Proven experience building reusable ingestion and extraction patterns across REST and Graph APIs, batch and file processing, Kafka and event-driven integration, and incremental processing, with an understanding of SQL versus NoSQL trade-offs.
• Hands-on contribution to designing and building a data lake with raw, standardized or normalized, and curated consumption layers that scale to future sources and use cases.
• Advanced CI/CD and DevOps skills: automated testing, release strategies, infrastructure as code and environment management.
• Expert knowledge of cloud infrastructure and scalable architecture, including cloud-native services, containers, infrastructure as code, resiliency patterns, observability and secure enterprise deployment.
• Strong ability to investigate and resolve data discrepancies, missing or duplicate records, broken relationships, schema changes, ingestion failures, performance issues and source-to-target reconciliation problems, with suitable data quality controls.
• Experience designing curated datasets, APIs, scheduled extracts and event-based interfaces that let downstream applications and users consume data.
• Practical experience with coding copilots or LLM agents, and a clear view of how to bring them safely into enterprise workflows.
• Ability to own complex development independently while guiding junior and intermediate developers through solution design, development patterns, troubleshooting, code reviews, testing, documentation and knowledge sharing.
• Ability to explain constraints and trade-offs clearly to engineers, partners and leadership.
• 5-7+ years of relevant experience.
• An undergraduate degree, post graduate degree or technical certificate.
• A strong academic background, such as computer science or engineering.
• Preferred qualifications:
• Core stack: PostgreSQL, SQL, Python, REST/Graph APIs, Kafka, batch/file processing, Azure, Databricks/PySpark, Git and CI/CD, and knowledge graph or graph databases.
• Experience in data management, data governance, data cataloging or metadata platforms is a plus.
• Hands-on familiarity or integration work with data management, governance, metadata or catalog platforms such as DataHub, OpenMetadata, Collibra or comparable solutions is preferable.
• Practical knowledge of lineage and provenance, including OpenLineage, W3C PROV-oriented models and automated lineage capture, is preferred.
• Practical experience or exposure to Elasticsearch or OpenSearch concepts, or to Kafka or comparable message queue solutions.
• Practical knowledge of security and compliance engineering, including RBAC, ABAC, SSO/MFA, secrets management, audit logging, access governance, network controls and secure configuration baselines.
• Pandas experience for data analysis, profiling, transformation or automation is a plus.
• Deep experience designing and operating solutions across Azure, AWS or hybrid cloud, including managed services, container platforms and scalable infrastructure patterns.
• Familiarity with responsible use of coding copilots or AI-assisted developer workflows.
• A graduate degree is nice to have.

Conditions

Based in Toronto, Ontario, Canada.

Hours listed as 5.

Part of the Technology Solutions line of business.

Base pay range of $125,500 - $154,000 CAD.

This range includes a temporary market premium for this role, which is reassessed each year.

Pay offered may vary with skills, experience, job knowledge, location and business needs, and pay can progress through the range as you grow in the role.

Ask your recruiter any compensation questions; they can give you specifics for this role.

This job is subject to provincial employment regulation, and each province or territory in Canada may have its own rules.

Interviews are arranged with candidates of interest, and outcomes are shared by email or phone.

Benefits

• Total rewards include base salary, variable compensation, health and well-being benefits, savings and retirement programs, paid time off, banking benefits and discounts, career development, and reward and recognition programs.
• Regular career, development and performance conversations with your manager.
• Access to an online learning platform and a range of mentoring programs.
• Training and onboarding sessions to help you settle into the role.