Lead Data Analyst

Job Description

The Engineering team is driving multiple complex, enterprise-wide initiatives to build RBC's next-generation data platform — one that is AI-ready, scalable, and trusted across the organization. In this senior role, you will serve as a technical lead and strategic partner: shaping how data flows, how AI/ML systems consume it, and how analytics insights reach decision-makers. You will own end-to-end delivery — from requirements through production — while mentoring teams and influencing platform direction.

• Lead requirements definition and translate complex business needs into precise technical specifications: data contracts, transformation logic, AI/ML feature requirements, non-functional requirements, and acceptance criteria.
• Drive deep-dive analyses on customer behavior, product performance, campaign outcomes, and channel effectiveness — with a lens toward AI-augmented insight generation and predictive opportunity identification.
• Architect, build, and own dashboards, scorecards, and executive reporting frameworks; define standards for how data products are presented to senior leadership.
• Act as a technical bridge between business stakeholders, engineering, and data science teams — validating source-to-target mappings, enforcing data quality, and ensuring AI/ML pipelines consume reliable, well-governed data.
• Lead production readiness reviews, post-implementation validation, and continuous improvement cycles to ensure solutions are accurate, stable, and performing at scale.
• Mentor and guide junior analysts and engineers; establish best practices for analytics engineering, data quality, and AI-ready data design across the team.

• 10+ years of progressive experience as a data analyst, analytics engineer, or senior business systems analyst — with a track record of delivering at enterprise scale.
• Proven ability to lead complex, cross-functional data initiatives from ambiguous requirements through production delivery.
• Deep expertise in data mapping, acceptance criteria definition, UAT leadership, and production validation for analytics or data platform solutions.
• Expert-level SQL: complex multi-table joins, window functions, query optimization, and performance tuning on large enterprise datasets.
• Strong understanding of AI/ML workflows and how data platforms must be designed to support feature engineering, model training pipelines, and real-time inference.
• Hands‑on knowledge of Kafka, schema registries, and event streaming concepts — including schema evolution, data contracts, and event quality validation.
• Deep familiarity with modern data platform architectures: data warehouses, Lake houses (e.g., Delta Lake, Iceberg), and how they serve both BI and AI use cases.
• Exceptional stakeholder communication skills: able to translate technical complexity into clear narratives for senior and executive audiences.

• Domain experience in financial services — banking, credit data, or regulatory reporting.
• Familiarity with LLM/GenAI integration patterns: RAG pipelines, embedding workflows, or AI-assisted analytics.
• Experience with Git Hub Actions and CI/CD for data pipelines.
• Knowledge of Debezium, GraphQL, or ELK Stack (Elasticsearch / Logstash / Kibana).
• Hands‑on experience with cloud‑native platforms:
  Open Shift, Kubernetes, S3 object storage.
• MongoDB experience: querying semi-structured data, aggregation pipelines for analytics use cases.
• Proficiency with BI tools (Tableau, Power BI) and data quality frameworks for trusted, governed reporting.

We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.

• A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation and pension plan.
• Leaders who support your development through coaching and managing opportunities.
• Work in a dynamic, collaborative, progressive and highly performing team.
• Opportunities to do challenging work, making a difference and lasting impact on communities.
• Enjoy a comfortable work environment with the option to dress casually.
• Network and build lasting relationships with developers from diverse backgrounds from across Canada and the world.

• Big Data Management
• Critical Thinking
• Data Administration
• Data Mining
• Data Modeling
• Data Movement
• Detail-Oriented
• Group Problem Solving
• Quantitative Research
• Research Documents

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