Data Analyst

RBC•Toronto, ON

4d•Onsite

About The Position

The Data Analyst is responsible for hands-on data analysis, profiling, validation, and quality assurance across the CDR and broader Client Data ecosystem. This role is the engine room of the team, ensuring that client and counterparty data is accurate, complete, consistent, and fit for purpose across regulatory, risk, and business use cases. You will work across multiple source systems and datasets, performing detailed investigations into data issues, supporting onboarding and migration initiatives, executing reconciliations, and contributing to the team's data quality frameworks. The ideal candidate is analytically rigorous, detail-oriented, and comfortable navigating messy, multi-source data environments. You take ownership of your workstreams and deliver reliable, well-documented analysis with minimal oversight.

Requirements

3 to 5 years in a Data Analyst or similar data-focused role, with experience working across multi-source datasets in a complex environment.
Demonstrated ability to own and deliver analytical workstreams independently within larger programs.
Experience with data quality, reconciliation, or migration initiatives is strongly preferred.
Strong SQL skills with experience writing complex queries against large relational databases (SQL Server preferred).
Comfortable with joins, window functions, CTEs, and subqueries.
Strong proficiency in Excel including advanced functions, pivot tables, lookups, and Power Query.
Experience with data profiling, validation, reconciliation, and standardization techniques.
Exposure to Python, R, or VBA for data manipulation is an asset.
Comfort working across messy, incomplete, and inconsistent datasets, and applying structured approaches to make sense of them.

Nice To Haves

Experience with data quality, reconciliation, or migration initiatives is strongly preferred.
Exposure to Python, R, or VBA for data manipulation is an asset.

Responsibilities

Perform data profiling, exploratory analysis, and validation across large, multi-source datasets to assess completeness, accuracy, and consistency.
Execute end-to-end analytical work in support of data onboarding, migration, reconciliation, and quality remediation initiatives.
Investigate data anomalies and discrepancies, tracing issues across source systems, transformation logic, and target platforms.
Deliver structured, well-documented analysis that is clear enough for stakeholders to review and for developers to act on.
Execute data quality checks against defined rules, thresholds, and business expectations.
Perform root cause analysis on data breaks and exceptions, identifying whether issues are systemic or isolated.
Recommend and track remediation actions, pushing toward structural fixes rather than recurring manual corrections.
Contribute to the definition and refinement of data quality rules and exception handling processes.
Execute reconciliation between source systems and target platforms, documenting discrepancies with clear supporting evidence.
Validate data transformations by comparing expected versus actual outputs, flagging mismatches and working with technology teams to resolve them.
Ensure reconciliation results are reproducible, well-documented, and traceable.
Contribute to the documentation and validation of data mapping and transformation logic across source and target systems.
Support the team in maintaining data lineage documentation to ensure traceability and auditability.
Verify that implemented data flows match business specifications, identifying gaps and escalating where needed.
Maintain clear, structured documentation including analysis logs, reconciliation results, data dictionaries, and mapping specifications.
Ensure all work products are reproducible and well organized for handoff or review.
Contribute to the development and improvement of team standards for analysis, documentation, and quality assurance.
Work closely with Business Analysts and Senior Analysts to support requirements validation through data samples, profiling results, and feasibility assessments.
Participate in working sessions with stakeholders and technology teams, contributing data-level insights.
Respond to ad hoc data requests from stakeholders, delivering accurate and timely outputs.
Communicate findings clearly, escalating issues with context and recommended next steps.