Senior Analyst, Payments Data Intelligence

| What we are looking for

The Senior Analyst: Payments Data Intelligence (PDI) serves as the senior technical lead and quantitative subject matter expert responsible for the hands-on design, execution, and validation of PDI’s analytics engine and predictive modeling workflows. Positioned strategically between analytical support and the PDI Manager, this role owns the technical and statistical integrity of multi-rail payment datasets.

The Senior Analyst operates with full technical autonomy over codebases, complex SQL transformations, Python feature engineering, GCP data pipelines, and MLOps back testing suites. This role transforms high-level business queries and scenario specifications into production-ready analytical pipelines, automated data quality controls, and validated quantitative outputs. Acting as a genuine analytical thought-partner who understands the statistical significance and underlying data structures behind feature choices, this role ensures data readiness, maintains the analytics environment, validates model inputs, and supports repeatable workflows across multi-rail payment data (Lynx, ACSS, RTR, and other external payment rails). This individual is a self-directed, highly curious professional capable of bridging technical execution with strategic product goals, working cross-functionally with Data Engineering and Product teams to transform multi-source payment data into trusted, production-ready assets.

Technical Data Ingestion Automated Quality Control (Data Layer Ownership)

• Own the technical design and execution of complex SQL/Python scripts to ingest, transform, and normalize multi-rail transaction data and exogenous feeds.
• Design and maintain automated data validation pipelines, schema enforcement, anomaly flags, and reliability score tagging prior to modeling ingestion.
• Build and optimize clean, scalable, and documented multi-rail data structures within Google Cloud Platform (BigQuery) to support ongoing cross-rail intelligence.

Data Preparation and Modelling Readiness

• Assemble and prepare multi-rail payment data (Lynx, ACSS, RTR) and relevant external/exogenous datasets so they are structured for time-series and modelling work.
• Perform data cleansing, normalization, and reconciliation to ensure model inputs are consistent, accurate, and trustworthy.
• Build repeatable, automated checks for model input quality, completeness, consistency, and anomaly flags so issues are identified prior to modelling.
• Establish clean, well-documented, repeatable workflows for bringing new internal and external data sources into the PDI modelling pipeline.

Data Analytics

• Develop an understanding of the payments business to produce business insights, discover patterns and interpret trends stemming from data analysis.
• Lead hands-on time-series decomposition, cross-rail migration analytics, correlation mapping, and advanced feature engineering.
• Develop and code reproducible analytical pipelines that model volume, value, structural shifts, and payment velocity across rails.
• Prepare datasets and analytical outputs for back-testing, model performance evaluation, and stress/scenario analysis.
• Code reproducible analytical pipelines that model volume, value, structural shifts, and payment velocity across rails.
• Produce analytical judgment on data structure and feature choices, providing a critical second set of eyes on the data side of modelling decisions.
• Conduct the integration of model outputs (such as explainability/attribution views and pricing-related outputs) into downstream business tools and executive reporting.
• Develop and maintain clear and concise documentation on process, data sources, and models development

Environment and Technical Workflow

• Manage day-to-day analytics environment configuration (Big Query, Cloud Storage, Vertex AI tooling and related tools), query performance, and computational efficiency escalating complex security or platform work appropriately.
• Maintain the technical workflow that transitions models into production, including basic automation for repeatable deployment steps.
• Ensure and maintain reliable input-quality checks and basic performance monitoring for production models so performance issues are caught early.
• Facilitate the integration of model outputs (e.g., explainability, attribution, pricing outputs) into downstream business tools.
• Establish and enforce SQL/Python coding standards, documentation practices, and reproducible analytical workflows.
• Author technical data documentation, model validation summaries, and methodology briefs supporting Stage Gate reviews.

Cross-Functional Alignment and Stakeholder Delivery

• Work day-to-day in close partnership with the Lead Data Scientist to prioritize analytical and preparatory work in support of the Product roadmap and PDI strategy.
• Collaborate cross-functionally with the Data Engineering Team to ensure pipeline compatibility and alignment with platform architecture.
• Translate complex technical and statistical findings into plain language for the Director and key product stakeholders.

Technical Skills

• Advanced ability to query, transform, normalize, and validate complex multi-rail transaction datasets independently.
• Solid capability in Python for data preparation, feature engineering, statistical analysis, and reproducible workflow automation.
• Grounded understanding of time-series concepts, correlation/variable analysis, back-testing, and model validation techniques.
• Working familiarity with Google Cloud Platform tools (BigQuery, Cloud Storage) to maintain analytics environments and query large-scale payment data.
• Familiarity with Vertex AI, model explainability frameworks, or automated deployment tooling.

Professional Competencies

• Develop and Mentor others: Supporting others to develop and learn, recognizing their achievements, and building an organizational culture where knowledge and growth are valued and rewarded.
• Demonstrate Mature Confidence: Demonstrating confidence and realistic trust in one's own abilities. Understanding and managing their own emotions and drives, and the impact of self on others to minimize counterproductive actions, instill trust and inspire others.
• Think Strategically: Seeing and synthesizing different sides of an issue, examining the full range of options and outcomes, and building intellectual frameworks to guide analysis and action.
• Make Decisions: Using critical thinking, good business sense and organizational values to make decisions involving varying levels of risk and ambiguity.
• Drive Success: Understanding the workings, structure, culture, as well as the distribution of power within and beyond the organization; utilizing this understanding to solve problems and achieve desired outcomes.
• Apply Business Acumen: Continually seeking new ways to enhance performance, improve results and/or transform the business and the industry in which it operates.

| What you need to be successful

• Undergraduate or graduate degree in Data Science, Computer Science, Mathematics, Statistics, Economics, Finance, or a related quantitative discipline.
• Minimum of five (5) years of experience in data analytics, data science support, or quantitative analysis.
• Strong statistical grounding with comfort in time-series concepts, correlation analysis, and feature engineering to act as a thought-partner to data scientists.
• Solid technical proficiency in Python and SQL for data preparation, transformation, and validation.
• Working familiarity with cloud analytics environments (BigQuery or equivalent) with explicit enthusiasm to master Google Cloud Platform tools.
• Hands-on experience building or supporting data validation, cleansing, normalization, and quality control processes.
• Ability to translate complex technical work into clear, non-technical briefings for leadership and cross-functional teams.
• Self-directed mindset with high ownership, strong problem-solving orientation, and comfort navigating ambiguity.
• Exposure to Vertex AI or model-deployment tooling, workflow automation tools, model explainability techniques, or prior exposure to payment market infrastructure (Lynx, ACSS, RTR) is considered as an asset.
• Eligibility to obtain and maintain a Government of Canada Reliability Status Clearance and can successfully complete enhanced background checks that may be carried out by Payments Canada.

| Salary range

Our target starting rate for this role is $100,300 with flexibility based on your experience and qualifications. The full salary range and benefits package are detailed below.

Please submit your application by October 30, 2026.