Senior Analyst (Data Engineer)

Selection Process Number: 26-27-SIF-EA-JR100567 Division: Risk & Data Analytics Group and Level: RE 05 Salary Range: $105,200.00 - $135,300.00 Employment Tenure: Indeterminate Positions Available: 1 WHO CAN APPLY Persons residing in Canada, and Canadian citizens and Permanent residents abroad. IMPORTANT INFORMATION This poster will close at 12:00 AM EST on the posting’s indicated closing date (note that for those in the Pacific Time Zone, this corresponds to 9:00PM PST on the previous day). To be considered, your application must be submitted before the specified deadline. If you experience any technical issues submitting your application, please email a screenshot of the issue to StaffingHR-DotationRH@osfi-bsif.gc.ca . Due to weekly system maintenance, Workday will be unavailable for 3 hours every Friday from 11pm to Saturday 2am EST. Applicants must submit a resume as part of this job application. Do not include a cover letter as part of your submission. We are committed to providing an inclusive and barrier-free work environment, starting with the hiring process. If you need to be accommodated during any phase of the evaluation process, please use the Contact information below to request specialized accommodation. All information received in relation to accommodation will be kept confidential. Information on assessment accommodation. RESUME Applicants must submit a resume as part of this job application. POSITION DETAILS CLASSIFICATION This position is classified at the RE-05 group and level which is roughly equivalent to the CT-FIN-03 group and level. LOCATION This position is located in Toronto. Working from the Ottawa office may be approved, depending on worksite availability. OSFI’s work model includes telework and mandatory onsite presence. The terms of the employee’s telework arrangement will be established in accordance with the Directive on Telework and related organizational directions. These arrangements must be reviewed annually, at a minimum, and can be subject to change. PROCESS INTENT The immediate need is to staff RE-05 position with a language requirement of English essential on an indeterminate basis. A pool of qualified or partially qualified candidates may be created to staff similar or identical positions with various linguistic requirements and/or profiles, security requirements, tenures, and/or locations within the Office of the Superintendent of Financial Institutions (OSFI), which may vary according to the position being staffed. KEY RESPONSIBILITIES Duties: The Data Engineer will play a key role within the Data Engineering team, supporting the delivery of high‑quality, reliable, and well‑governed datasets. This role focuses on ensuring timely, accurate, and complete data through strong data engineering practices, data quality validation, and analytical insight. The Data Engineer will contribute to building scalable pipelines, improving data quality processes, and enhancing the overall data ecosystem through continuous improvement and innovation: • Develop, maintain, and optimize data pipelines in Azure Synapse / Microsoft Fabric to support high-quality data ingestion, transformation, and validation. • Extract and integrate data from APIs and other structured or semi-structured sources as part of automated data quality workflows. • Perform data profiling, cleansing, and standardization to identify anomalies, inconsistencies, and data quality issues across large datasets. • Apply data lake best practices, including privacy and security controls such as masking, anonymization, and secure handling of sensitive data. • Support data migration activities by validating data completeness, accuracy, and consistency across environments and systems. • Use Python and Spark to build scalable data transformations, automation scripts, and data quality routines. • Leverage Azure Logic Apps and Azure Functions to automate workflows and support event-driven data processing. • Use DevOps for Git repository management, CI/CD pipelines, YAML-based definitions, and automated deployment of data engineering artifacts. • Implement environment-specific configuration management using variables, parameter files, and secure key handling. • Apply automated testing approaches for data pipelines, including unit tests, data quality checks, and regression validation. • Design solutions that support auditability, traceability, and data retention requirements. ESSENTIAL QUALIFICATIONS Official Language Proficiency English Essential Information on language requirements In order to be considered, your application must clearly explain how you meet each of the following education and experience criteria. ESSENTIAL EDUCATION • A degree OR diploma from a recognized post-secondary institution with specialization in business, commerce, economics, statistics, mathematics, data analytics, computer science, engineering or other relevant field OR an acceptable combination* of relevant experience AND education or training. *At the manager’s discretion, OSFI may consider candidates who do not possess a degree or diploma but meet the combination of experience and education or training if concrete examples are provided. Information on degree equivalency ESSENTIAL EXPERIENCE • Recent (1*) and significant (2*) experience developing, maintaining, and optimizing data pipelines in Azure Synapse or Databricks to support high‑quality data ingestion, transformation, and validation. • Recent (1*) experience performing data profiling, cleansing, and standardization to identify anomalies, inconsistencies, and data quality issues across large datasets. • Recent (1*) experience working with cloud data platforms (e.g., Azure Synapse, Databricks, Fabric, or similar) including data lake concepts, privacy, secure handling of sensitive data. • Recent (1*) experience using Python and Spark to build scalable data transformations, automation scripts, and data quality routines. • Recent (1*) experience with Devops Practices including Git repository management, CI/CD pipelines and automated deployment of data engineering artifacts. • Demonstrated experience (3*) applying automated testing approaches for data pipelines, including unit tests, data quality checks, and regression validation. • Demonstrated experience (3*) integrating data from various source systems like sftp, APIs etc. and other structured or semi‑structured sources. • Experience (3*) working with banking or financial services data such as regulatory, risk, compliance, payments or reporting datasets across typical industry domains. *NOTE (1*) Recent is as ANY relevant experience gained within approximately the last three (3) years. (2*) Significant is defined as the depth and breadth of the experience normally associated with the performance of the duties for a period of five (5) years. (3*) Experience is understood to mean the depth and breadth of experience normally associated with having performed a broad range of related activities. The amount of complexity and the diversity of tasks, as well as the autonomy level will be taken into consideration. The following essential knowledge, competencies and abilities will be assessed at a later date. ESSENTIAL KNOWLEDGE • Knowledge of diverse data source systems and methods for extracting data from platforms such as SFTP, APIs, databases, and file‑based sources. • Knowledge of designing and building data pipelines, including orchestration, transformation, and end‑to‑end workflow management. • Knowledge of framework for Data ingestion and Data Quality covering extraction, validation, monitoring and standardizing process. • Knowledge of working with structured, semi‑structured, and unstructured dataset. • Knowledge of data engineering best practices for designing and maintaining optimized, reliable, and scalable data pipelines. ESSENTIAL COMPETENCIES • Collaboration • Inn