What is the opportunity?
The Principal, Data Engineering and Architecture is a senior individual contributor who leads the end-to-end engineering of enterprise-grade data applications, combining hands‑on development with strategic architecture. Working at the intersection of Data architecture, engineering, and modern software delivery, this role is directly accountable for engineering robust, scalable solutions, operationalizing Architecture as Code, and embedding Agentic AI capabilities into the enterprise data ecosystem, with robust CI/CD integration.
Job Description
The Principal, Data Engineering and Architecture is a senior individual contributor who leads the end-to-end engineering of enterprise-grade data applications, combining hands‑on development with strategic architecture. Working at the intersection of Data architecture, engineering, and modern software delivery, this role is directly accountable for engineering robust, scalable solutions, operationalizing Architecture as Code, and embedding Agentic AI capabilities into the enterprise data ecosystem, with robust CI/CD integration.
The incumbent will collaborate with cross‑functional teams - data engineers, product managers, ML engineers, and business stakeholders to accelerate innovation through hands‑on engineering, infrastructure‑as‑code, and production‑ready deployments. This role sets technical direction across multiple teams, establishes engineering standards (opinionated frameworks, patterns, and reusable templates), and ensures every data solution is built with security, observability, and operational resilience by design. The principal will conceive and portray the big picture for enterprise data, analyze current state, conceptualize desired future state with the Enterprise Platforms, and define the architecture governance and solution roadmap to close the gap.
As a practicing data architect, the incumbent will design data product reference architectures, formalize Data Contracts as governed architecture artifacts, and position the Semantic Layer as a first‑class tier in the enterprise stack, codifying these patterns into the TRM and Reference Architectures, while conducting architecture reviews to ensure domain teams build in conformance.
What will you do?
- Hands‑on engineering of high‑quality data and AI applications end‑to‑end, from conceptual design through build, deployment, and production hardening.
- Build the enterprise data management framework that defines how data is stored, consumed, integrated, and governed by different data entities and applications — ensuring alignment with the Enterprise Data Reference Architecture (ERDA).
- Drive the Data Architecture Strategy for domain‑oriented Data Hubs and Data Products, self‑service data analytics, with established best practices and capabilities for operationalizing data products and services.
- Design data product reference architectures — define the canonical patterns, blueprints, and boundary definitions that data product teams follow when building, publishing, and consuming data products across domains.
- Model and formalize Data Contracts as architecture artifacts — author contract specifications (schema, SLA, semantic) as versioned, machine‑readable architecture documents that sit alongside Architecture Decision Records and are governed through the same review process.
- Architect the Semantic Layer within the enterprise data architecture, to position semantic layer as an architectural tier in the enterprise stack, defining how metric definitions, business logic, and dimensional models are authored, versioned, and consumed consistently by BI, AI/ML, and application layers.
- Define data product boundary and integration patterns — codify architectural guidance on how data products expose interfaces (APIs, event streams, materialized views, semantic endpoints), how contracts govern those interfaces, and how consumers discover and bind to them.
- Create data product and contract standards — codify approved technologies, frameworks, and tooling for data product construction, contract definition, semantic layer implementation, and contract validation between producers and consumers — schema contracts (structure, types, constraints), SLA contracts (freshness, availability, latency), and semantic contracts (business definitions, lineage, classification) — validated automatically within CI/CD pipelines through contract testing.
- Architect and implement a unified Semantic Layer — a centralized business‑logic tier that provides consistent metric definitions, dimensions, and governed data access across BI tools, AI/ML models, and application APIs, eliminating metric drift and conflicting business definitions.
- Integrate contract testing and semantic validation into delivery pipelines — schema enforcement, backward‑compatibility checks, anomaly detection, freshness validation, and semantic consistency verification prior to stage promotion.
- Design the data product lineage and observability architecture — architect end‑to‑end traceability from source system through data product to consumer, including contract compliance monitoring, semantic layer usage telemetry, and SLA adherence dashboards.
- Automate architecture contract for data product designs — review domain team data product proposals for conformance to enterprise patterns, contract completeness, semantic layer alignment, and integration with the broader data architecture.
- Evolve the Enterprise Data Reference Architecture (ERDA) to formally incorporate data‑as‑product principles, contract‑driven integration, and the semantic layer as first‑class architectural constructs alongside traditional storage, processing, and governance tiers.
- Enable and operationalize Architecture as Code for data initiatives, Data Hubs, and Data Products - codify hub architecture topologies and design, data product boundaries, contract definitions, and integration patterns as version‑controlled architecture definitions that are validated on every change, ensuring data initiatives are architecturally governed from inception through production deployment.
- Design and deploy multi‑step reasoning workflows and autonomous decision agents for Architecture— including retrieval‑augmented generation (RAG), embedding‑based search, prompt engineering, and agent evaluation/guardrails frameworks to ensure agents behave reliably, safely, and within business policy constraints.
- Hands‑on POC development for novel agentic patterns to prove viability, inform adoption, and scale successful patterns into production platforms.
- Apply AI‑assisted tooling to enhance architecture decisioning, documentation, code generation, and operational optimization — while validating outputs with sound engineering judgment.
- Conceptualize and influence solutions in major programs that steer toward desirable outcomes, in alignment with the Enterprise Data Architecture Strategy.
- Set technical direction and influence across multiple teams to align stakeholders on trade‑offs, sequencing, and engineering standards.
- Mentor and coach architects, and practitioners; serve as the technical escalation point for engineering challenges in the Data Architecture applications.
- Lead cross‑team design reviews, create reusable architecture artifacts, and contribute to organization‑wide engineering standards and best practices.
- Collaborate with senior technology and business leaders to articulate trade‑offs, influence roadmaps, and align platform strategy with the enterprise’s long‑term data and AI vision.
What do you need to succeed?
Must Have
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
- 10+ years as a software/platform engineer, with a track record of designing and delivering large‑scale distributed data and AI platforms.
- Practical experience with Architecture as Code (AaC) — defining and maintaining system architecture in version‑controlled, machine‑readable formats (Finos CALM, DSL, or equivalent), integrated with CI/CD validation and automated diagram generation.
- Practical experience building and deploying Agentic AI systems — LLM‑based agents, multi‑step reasoning workflows, tool‑use patterns, RAG architectures, embedding‑based search, and agent orchestration frameworks (LangGraph, CrewAI, AutoGen, or equivalent).
- Strong engineering fundamentals — proficiency in Python, Java, or Go; infrastructure‑as‑code (Terraform); containerization (Docker); policy‑as‑code; and modern API design (REST, gRPC, GraphQL).
- A practitioner of both Enterprise Data Architecture and Solution Architecture with a track record of meeting both program and delivery outcomes based on their architectures.
- Grounded in software and system engineering design principles for data and integration architecture, security, automation, orchestration, and Data Observability Engineering.
- Working experience with multiple data storage and analysis toolsets — including cloud object stores (Blob/S3), data processing (ADF/Glue/Databricks, Snowflake, Starburst Trino), and streaming (Kafka/Kinesis).
- Strong DevOps foundation — CI/CD, containerization, observability and telemetry, SLA‑driven reliability engineering, and secure cloud operations (secrets management, IAM, encryption, network security).
- Demonstrated experience influencing senior technical and business leaders, with the ability to clearly articulate engineering trade‑offs and drive cross‑team decisions.
- Experience with Agile methodologies, continuous improvement mindset, and security‑first development practices.
- Strong leadership skills and demonstrated experience with programs/projects involving complex integration of technologies/platforms across functional technical teams.
- Demonstrated ability in written and oral communication skills along with strong presentation skills.
Nice to Have
- Familiarity with multi‑agent orchestration patterns — task decomposition, tool‑use pipelines, agent memory management, and human‑in‑the‑loop workflows at enterprise scale.
- Experience with agent evaluation frameworks, safety guardrails, and responsible AI deployment in production.
- Experience with data quality and contract validation frameworks — Great Expectations, Soda, Monte Carlo, or equivalent tools for data observability and automated contract enforcement.
- Experience with data governance and catalog platforms — Collibra, or equivalent for metadata management, stewardship, and policy enforcement.
- Contributions to open‑source engineering or AI projects; active participant in the broader engineering community.
- Cloud cost optimization and FinOps experience for data and AI.
What’s in it for you?
- A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock where applicable
- Leaders who support your development through coaching and managing opportunities
- Ability to make a difference and lasting impact
- Work in a dynamic, collaborative, progressive, and high‑performing team
- A world‑class training program in financial services
- Flexible work/life balance options
- Opportunities to do challenging work
- Opportunities to take on progressively greater accountabilities
- Opportunities to building close relationships with clients
- Access to a variety of job opportunities across business and geographies
#TECHPJ
Job Skills
Architectural Modeling, Critical Thinking, Data Architecture Development, Data Warehousing (DW), Decision Making, Enterprise IT Architecture, Information Technology Consulting, Information Technology Trends, Multi‑Level Communication, Physical Database Design
Additional Job Details
Address: RBC WATERPARK PLACE, 88 QUEENS QUAY W:TORONTO
City: Toronto
Country: Canada
Work hours/week: 37.5
Employment Type: Full time
Platform: TECHNOLOGY AND OPERATIONS
Job Type: Regular
Pay Type: Salaried
Posted Date: 2026-07-05
Application Deadline: 2026-09-30
Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above