Senior Analyst, Payments Data Intelligence

Role overview

Data ingestion and quality

• Design SQL and Python ingestion, transformation, and normalization for multi-rail transaction data and external feeds.
• Build automated data validation pipelines, schema enforcement, anomaly flags, and reliability score tagging before modeling ingestion.
• Build scalable, documented multi-rail data structures in Google Cloud Platform and BigQuery.
• Prepare, cleanse, normalize, and reconcile payment and external datasets for time-series and modeling work.
• Automate checks for model-input quality, completeness, consistency, and anomalies; document repeatable workflows for new data sources.

Analytics and modeling support

• Analyze payment data to produce insights and identify patterns and trends.
• Lead time-series decomposition, cross-rail migration analytics, correlation mapping, and advanced feature engineering.
• Build reproducible pipelines modeling payment volume, value, structural shifts, and velocity across rails.
• Prepare datasets and analytical outputs for back-testing, model evaluation, and stress or scenario analysis.
• Integrate model explainability, attribution, and pricing outputs into business tools and executive reporting.
• Document processes, data sources, and model development.

Technical workflow and collaboration

• Manage analytics environment configuration, query performance, and computational efficiency across BigQuery, Cloud Storage, Vertex AI tooling, and related tools.
• Maintain model production workflows, repeatable deployment automation, input-quality checks, and basic performance monitoring.
• Establish SQL and Python coding standards, documentation practices, and reproducible analytical workflows.
• Prepare technical data documentation, model validation summaries, and methodology briefs for Stage Gate reviews.
• Partner with the Lead Data Scientist, Data Engineering, Product, and stakeholders on priorities, pipeline compatibility, and clear communication of findings.

Qualifications and experience

• Undergraduate or graduate degree in Data Science, Computer Science, Mathematics, Statistics, Economics, Finance, or a related quantitative field.
• At least five years of data analytics, data science support, or quantitative analysis experience.
• Strong statistical grounding in time-series concepts, correlation analysis, and feature engineering.
• Proficient in Python and SQL for data preparation, transformation, and validation.
• Working familiarity with BigQuery or equivalent cloud analytics environments; enthusiasm to master GCP tools.
• Hands-on experience with data validation, cleansing, normalization, and quality control processes.
• Able to explain complex technical work in clear, non-technical briefings; self-directed, high ownership, and comfortable with ambiguity.
• Vertex AI, deployment tooling, workflow automation, model explainability, or payment infrastructure exposure is an asset.
• Eligible to obtain and maintain Government of Canada Reliability Status Clearance and complete enhanced background checks.

Technical skills

• Python, SQL, statistical analysis, time-series concepts, correlation analysis, and feature engineering.
• GCP tools including BigQuery and Cloud Storage; Vertex AI or equivalent tooling is an asset.
• Data ingestion, transformation, normalization, validation, quality control, back-testing, and model validation.
• Payment rails include Lynx, ACSS, RTR, and other external payment data.

Work arrangement

• Flexible hybrid remote and office environment.

Compensation

• Target starting rate is CAD 100,300, with flexibility based on experience and qualifications.
• Competitive package includes an annual variable bonus and a defined contribution pension plan with employer matching, if eligible.

Benefits

• Health and dental coverage, mental health coverage, life insurance, and a health spending account, subject to stated employee eligibility.
• At least four weeks of vacation, sick and personal days, December holiday shutdown, and cultural holiday observance days.
• Paid parental leave top-up, if eligible; rewards and recognition; office gym; and professional development opportunities.