Senior Analyst (Data Engineer)

Selection Process Number

26-27-SIF-EA-JR100567 Division and Group

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. Due to weekly system maintenance, Workday will be unavailable for 3 hours every Friday from 11pm to Saturday 2am EST. Position Details

Classification: RE-05 group and level which is roughly equivalent to the CT-FIN-03 group and level. Location: 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. Key Responsibilities

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 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 does not possess a degree or diploma but meet the combination of experience and education or training if concrete examples are provided. 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. Important Notes on Experience

*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. 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 Innovation Critical Thinking Results Orientation Essential Abilities

Ability to communicate effectively in writing. Ability to communicate effectively verbally. Asset Qualifications

Asset Education

A relevant recognized professional designation Asset Experience

Recent (1*) experience designing and implementing data lake architectures, including medallion (Bronze/Silver/Gold) patterns and secure handling of sensitive datasets. Experience (2*) supporting data migration initiatives, including schema mapping, reconciliation, and validation across multiple environments. Experience (2*) implementing data validation frameworks, automated testing approaches, or data quality monitoring processes within data pipelines. (1*) Recent is ANY relevant experience gained within approximately the last three (3) years. (2*) 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. Asset Knowledge

Knowledge on integrating data from APIs or other structured/semi‑structured sources into enterprise data environments. Asset Abilities

Ability to quickly learn new tools and techniques, including those from open-source software. Operational Requirements

Ability and willingness to work overtime. Ability and willingness to travel within Canada when required. Conditions of Employment

Security – Reliability In our hybrid workplace environment, the ability to work remotely from home within Canada with access to the Internet in one’s residence is a condition of employment. Organizational Needs

OSFI is committed to having a skilled and diverse workforce representative of the Canadian population. In order to meet our employment equity objectives, selection for this position may be made from among qualified candidates who self‑declare as belonging to one or more of the following Employment Equity groups: Persons with a disability, Indigenous Peoples, Members of a Visible Minority, or Women. OSFI is committed to diversity and inclusion, and we strongly encourage candidates to self‑declare if they belong to one of these designated employment equity groups. Preference will be given to veterans first and then to Canadian citizens and permanent residents, with the exception of a job located in Nunavut, where Nunavut Inuit will be appointed first. We thank those who applied; however, only candidates selected for further consideration will be contacted. Other Information

Organizational needs may be applied first, therefore, OSFI affected/opting employees may be given first consideration over other applicants. Please note that in some cases deployments may also be considered for the retention of employees. The Public Service of Canada is committed to building a skilled and diverse workforce that reflects the Canadians we serve. We promote employment equity and encourage you to indicate if you belong to one of the designated groups when you apply. Employment equity - Canada.ca. Preference will be given to veterans first and then to Canadian citizens and permanent residents, with the exception of a job located in Nunavut, where Nunavut Inuit will be appointed first. Information on the preference to veterans. We thank those who applied; however, only candidates selected for further consideration will be contacted. Eligible candidates may be considered and offered a deployment before considering other applicants. A DEPLOYMENT is a permanent movement of an employee at level or equivalent level within an organization to another organization in the core public administration, the OAG, or to the five separate agencies named in Schedule V to the FAA whose appointments are made in accordance with the Public Service Employment Act (PSEA). A deployment does not constitute a promotion or change a person's period of employment from a specified term to indeterminate. The use of generative artificial intelligence (AI) tools, sites, and applications based on large language models (e.g. ChatGPT, Dall‑E, Microsoft Copilot), is permitted for this application. You must cite any AI tool used by identifying the AI-generated content and its source. OSFI is a separate agency with its own classification and compensation system. OSFI's staffing is subject to the Public Service Employment Act (PSEA). All written and verbal communication obtained throughout the staffing process, from the time of application to close of process, may be used to evaluate the candidate. Candidates must meet all of the essential qualifications to be appointed; however, depending on the requirements of the specific position(s) being staffed, one or more asset criteria or organizational need may be invoked at any stage of the process. Staffing strategies such as random selection, top-down approach and/or establishing cut-off scores to determine who will continue in the staffing process, may be used for the purpose of managing applications. Please note that although you may attain the established pass mark on any of the assessments used in this staffing process, management may decide to use a higher cut off score. During the staffing process, various assessment methods can be utilized, including but not limited to written exams and interviews. These assessments may be conducted either remotely or in‑person at one of our OSFI offices (Toronto, Ottawa, Montreal or Vancouver). Candidates are entitled to participate in the appointment process in the official language of their choice. Email correspondence will be the primary method of communication with candidates for this selection process. Ensure you check your spam folder for any communications from OSFI (osfibsif@myworkday.com). You must provide valid and updated contact information. All job applications must be submitted through Workday: OSFI’s recruiting platform. Failure to do so may mean that you are either not considered or eliminated from the process.

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