Data Engineer

• Providing the organization’s data consumers high quality data sets by data curation, consolidation, and manipulation from a wide variety of large scale (terabyte and growing) sources.
• Building high quality data pipelines and ETL processes that interact with terabytes of data on leading platforms such as Snowflake and BigQuery.
• Developing and improving our enterprise data marts by creating efficient and scalable data models to be used across the organization.
• Partnering with our analytics, data science, crm, and machine learning teams for data solutions.
• Responsible for an enterprise data mart integrity, validation, and documentation.
• Responsible for the data pipelines’ SLA and dependency management.
• Writing technical documentation for data solutions, and presenting at design reviews
• Solving data pipeline failure events and implementing sound anomaly detection
• Working with various teams from analytics to product owners and front end developers on tracking solutions and solving technical challenges
• Leading medium to large size projects in terms of writing technical specs and project planning
• Mentoring junior members of the team.

Senior Data Engineer

• Perform Data Architecture support for projects and enhancements, including crafting and executing data management, architecture, integration, and governance strategies
• Provide technical leadership for architecture, design, documentation, and maintenance of databases and related systems, including creating database storage structures, resolving database issues, and maintaining system security
• Support development and implementation of business applications that optimize statistical efficiency and data quality
• Identify, analyze, and interpret trends or patterns in complex data sets
• Monitor daily operations of data loads, identify data discrepancies and data quality issues, and ensure data consistency and integrity
• Participate in project and enhancement estimation exercises and project team meetings, providing input, task status, risk identification, mitigation strategies, and issue management
• Collaborate with business partners to transform business requirements into environment-specific ETL, reporting, and data validation designs, and prioritize business and information needs
• Design relational databases and physical data models to support enterprise applications, data acquisition, and security requirements
• Review database changes to ensure no performance or functionality impacts and ensure production stability.

Senior Architect, Data

• Own Float's Snowflake architecture end-to-end — organization, cost efficiency, and access controls
• Design and build a real orchestration layer for our ingestion pipelines, replacing ad hoc processes
• Stand up observability and alerting so pipeline failures and data staleness are caught immediately, not downstream
• Introduce data contracts between producer and consumer systems, so upstream changes (new payment rails, new banking products, vendor field changes) stop silently breaking things
• Own the DataOps layer — establish CI/CD pipelines for data code, build data quality checks directly into pipelines, and orchestrate jobs that live outside of ingestion and dbt (ML pipelines, data quality runs, operational workflows), so everything is tested, automated, and deployed consistently
• Partner closely with our Analytics Engineer to keep the ingestion → transformation handoff clean and dependable — and collaborate on lineage tracking and metadata tooling so the broader team has visibility into where data comes from and how it flows
• Develop, document and socialize a target-state data architecture and phased migration roadmap, in partnership with data and engineering leadership.
• Build and maintain the data pipelines that power ML model training and inference — ensuring the data science team has reliable, well-structured feature data for ML use cases
• Leverage AI tooling across the full engineering lifecycle — from writing and reviewing pipeline code to accelerating observability, anomaly detection, and incident triage
• Partner with product and infrastructure engineering to maintain consistent telemetry standards across Float's codebases, ensuring product events flow reliably into the data platform.

• Design, develop, and maintain data pipelines using Microsoft Fabric and Azure Data Factory (ADF)
• Ensure data security and compliance by implementing best practices
• Collaborate with data governance stakeholders to enforce data quality standards
• Design and implement monitoring solutions using Azure-native services
• Deliver data solutions in alignment with approved architecture
• Collaborate cross-functionally with Data Architects, Business Stakeholders, and data teams
• Develop, configure, and maintain CI/CD pipelines using Azure DevOps
• Produce and maintain comprehensive documentation in Confluence
• Ensure adherence to ITIL-based processes for incident and change management
• Proactively drive operational excellence by identifying recurring issues
• Conduct peer code reviews through pull requests
• Deliver against project commitments

Senior Data Engineer – 6 month contract

• Design and optimize data models, feature stores, and storage patterns to ensure performance, reliability, and governance across AI workloads
• Build and maintain scalable batch and streaming data pipelines that ingest, transform, and curate high-quality datasets for AI and agentic workflows
• Implement DataOps and MLOps automation, including CI/CD pipelines, data validation, ML model deployment, monitoring, and drift detection
• Write production-quality code, conduct thorough code reviews, troubleshoot performance issues, and continuously enhance system reliability, scalability, and observability

Data/AI Engineer

• Drive the execution of the technical vision of our internal Data Infrastructure.
• Evolve the maturity and the level of technical excellence across your group and the entire EIM organization.
• Build an all-inclusive data program that is source agnostic and brings together data from various business applications to provide product usage, marketing effectiveness, employee retention, LTV, all in one place.
• Create true agility within the data team to work sprint-by-sprint and build progressively, embracing the concept of MVP and backlogs.
• Design and implement strategies that will achieve growth and productivity through process automation of existing and new operating models.
• Create data models, data mapping and the necessary ETL design. Build and validate activities for various data integrations.
• Partner with key stakeholders teams to deliver a scalable next generation data architecture, and solid data engineering processes that align with corporate Security and Data Governance.
• Foster data-driven culture and the creation of related competencies and data literacy across the organization.

Senior Data Engineer, EIM

• Define, document, and maintain enterprise AI services, capabilities, standards, patterns, and reference architectures.
• Develop reusable architectural building blocks supporting Generative AI, Agentic AI, orchestration frameworks, retrieval architectures, model access patterns, and AI governance controls.
• Evaluate emerging AI technologies, vendor capabilities, and platform offerings to determine suitability for enterprise adoption.
• Drive architecture reviews, governance approvals, risk assessments, and design reviews for AI-related initiatives.
• Collaborate with architecture review boards, technology governance forums, and risk stakeholders to achieve timely approvals.
• Partner with business groups to understand emerging needs, challenges, and strategic opportunities.
• Maintain detailed roadmaps, service backlogs, architecture repositories, and governance artifacts.

Senior Enterprise Data Architect

• Design, develop, and maintain scalable ETL/ELT pipelines using Azure Data Factory, Azure Databricks, and other relevant services
• Develop and maintain the company's data architecture, including data models, schemas, and data dictionaries
• Integrate data from various sources, ensuring data quality and consistency, and prepare it for analytical and operational use
• Follow consistency of data governance policies and processes to ensure data security, privacy, and compliance
• Collaborate with other data professionals, including data scientists, analysts, and business team members, to understand data requirements and develop solutions to meet those needs
• Stay up to date with the latest tools and technologies and see opportunities to enhance Manulife's data infrastructure
• Ability to learn and implement best practices of big data and cloud computing to ensure solutions are scalable, efficient, and effective

• Contribute to a cloud ops platform that enables ingestion of data from internal and external producers
• Compute and aggregate meaningful, curated data sets
• Surface these sets to Workday Front-End components or as embedded contextual data for AI/ML inference generation and agentic workflows

Senior Software Engineer – Data Hub

• Design, build, and maintain scalable and fault-tolerant data pipelines using AWS services.
• Own and evolve the architecture for data ingestion, transformation, and storage.
• Design and maintain data models, data warehouses, and data marts to support analytics and financial reporting.
• Develop and operate robust ETL processes ensuring timely, accurate, and auditable data ingestion.
• Partner with analytics, finance, product, and engineering teams to translate business requirements into durable data solutions.
• Optimize data pipelines and infrastructure for performance, reliability, and cost efficiency.
• Establish data quality checks, monitoring, and alerting to ensure trust in data.
• Ensure data security, integrity, and compliance across the full data lifecycle.
• Build and manage data infrastructure using Terraform and infrastructure-as-code practices.
• Support and improve executive dashboards, financial reporting, and operational analytics.
• Troubleshoot complex data issues and lead root cause analysis and resolution.
• Mentor junior team members and set best practices for data engineering across the organization.
• Stay current with industry trends and continuously improve data platform capabilities.

Intermediate Data Engineer

• Define and champion an integrated vision for unstructured data management, aligned with business objectives
• Make strategic decisions on preferred solutions, particularly in relation to XECM and Horizon initiatives
• Establish governance principles and guiding frameworks applicable to all teams
• Prioritize foundational use cases and make decisions when multiple options are available
• Ensure initiatives converge toward a coherent and sustainable goal
• Act as a cross-functional decision-maker on strategic direction, responsible for overall consistency
• Align stakeholders around decisions and mediate issues requiring consensus

Product Manager – Unstructured Data Platform

• Define the vision and roadmap for Wattpad's data platform
• Own the long-term strategy for the systems that transform raw product events into reusable, trusted capabilities that power products across Wattpad
• Develop and prioritize the roadmap across areas including:
• Reusable data products
• Insights Layer
• Decisioning platform
• Data governance and discoverability
• Define the strategy and roadmap for Wattpad's Product Intelligence Platform, enabling products to make smarter, faster, and more consistent decisions
• Build the platform capabilities that transform raw data into trusted, reusable intelligence for teams across the company
• Work with our Content teams to deepen our content intelligence, transforming story metadata, taxonomy, and behavioural signals into reusable capabilities that power products across Wattpad
• Partner with Engineering, Analytics, and Data Science to create scalable foundations for personalization, recommendations, and product decisioning
• Establish trusted metrics, shared data definitions, and governance practices that improve consistency and confidence across product development
• Turn recurring product needs into reusable platform capabilities that reduce duplication and accelerate delivery across teams
• Balance long-term platform investments with near-term business priorities, making thoughtful tradeoffs that maximize impact
• Influence product and technical strategy across the organization, helping shape the future of Wattpad's data and intelligence capabilities

Senior Product Manager, Data Platform – Intelligence

• This role works closely with Enterprise Architecture, Solution Architecture, Enterprise Data, Data Engineering, Data Science, Analytics, Infrastructure, Security, and Business teams to understand strategic priorities and define enterprise architecture solutions that support business outcomes.
• The role interacts closely with Data organizations across the enterprise and supports the delivery of client-centric business solutions through modern data management, analytics, and AI capabilities.
• Own, lead, and manage the overall Enterprise Data Architecture Strategy and Roadmap supporting Information Management, Data Management, Business Intelligence and Reporting, and Advanced Analytics.
• Develop and maintain a multi-year Enterprise Data Architecture strategy aligned with business priorities, technology modernization objectives, cloud adoption initiatives, and enterprise data transformation goals.
• Align with Sun Life's AI-ready Data Framework, and ensure that capabilities such as data quality, metadata, lineage, semantic models, governance, and data product standards mature effectively as required to support analytics and AI initiatives.
• Curate and prioritize enterprise data architecture investments to ensure alignment with business strategy and enterprise architecture objectives.
• Based on the tightly integrated nature of Data and AI, maintain joint accountability for developing and communicating integrated Data and AI roadmaps alongside AI Architecture and Engineering leaders.
• Support Data and Analytics organizations in translating strategic business requirements into scalable enterprise data architectures and enterprise information models.
• Define architecture standards and reference patterns supporting structured and unstructured data assets, including operational data, analytical data, documents, content repositories, and enterprise knowledge assets.
• Lead, coach, mentor, and develop a high-performing team of Directors, Architects, and Senior Consultants across geographies.
• Monitor regulatory, privacy, compliance, retention, audit, and governance requirements and assess their impact on enterprise information management practices.

AVP, Enterprise Data Architecture

• Own the commissioning plan and matrix
• Track execution of tests and commissioning milestones
• Maintain structured issue logs for commissioning
• Build and maintain Cx dashboards and reports
• Capture and analyze commissioning data
• Support documentation of commissioning artifacts
• Work closely with Cx leads

Commissioning Program, Data Engineer

• Design, develop, and maintain high-performance, secure, and scalable data pipelines (ETL/ELT) on Google Cloud Platform (GCP).
• Leverage GCP services such as: BigQuery (analytics data warehouse) Cloud Storage (raw data and data zones storage) Dataproc (distributed Spark/Hadoop processing) Cloud Run (deployment of data services and microservices)
• Participate in the implementation of modern data lakehouse architectures.
• Design and orchestrate data processing pipelines using Prefect.
• Implement robust, observable, and resilient workflows.
• Develop in modern environments (VS Code / Cloud Workstations).
• Manage source code using GitHub.
• Implement CI/CD pipelines with GitHub Actions.
• Automate deployments and infrastructure using Terraform (Infrastructure as Code).
• Prepare, transform, and structure data to support analytics and artificial intelligence use cases.
• Collaborate with data scientists to facilitate access to reliable and governed data.
• Implement DevOps/DataOps practices: Automated testing Pipeline monitoring Error handling and alerting Ensure data quality, traceability, and governance.
• Work collaboratively with: Product Owners and Product Managers (PO & PM) Architects, Data Scientists, Business partners
• Translate business needs into robust technical solutions.
• Document solutions and contribute to their continuous evolution.

Data Engineer, Investments

• Design and deploy data infrastructure needed to drive data-driven decision-making solutions
• Design and implement complex data orchestration models, modeling metadata, scaling reporting tools for data science and ML products users
• Be the company’s expert on data administration, data management and scalable data systems
• Write highly tuned, scalable SQL queries running over large-scale, heterogeneous data warehouses
• Work with the Product and Enterprise Engineering system teams to structure source systems for reporting consumption across the enterprise
• Help maintain CDC pipelines to power customer reporting
• Help develop front end applications to expose analytical data sets enterprise wide

• Design, build, and maintain scalable data pipelines and data platforms.
• Develop AI-powered data solutions using Agentic AI and Multi-Agent architectures.
• Build and optimize intelligent workflows using Snowflake Cortex capabilities.
• Integrate LLMs with enterprise data while ensuring governance and security.
• Design semantic search, RAG, and AI-assisted analytics solutions.
• Develop and optimize Snowflake data models, stored procedures, and performance tuning.
• Collaborate with Data Scientists, AI Engineers, Product Managers, and business stakeholders to deliver AI-enabled data products.
• Ensure data quality, reliability, observability, and security across data platforms.
• Automate deployment using CI/CD pipelines and Infrastructure as Code where applicable.
• Stay current with emerging AI, GenAI, and Snowflake technologies and recommend best practices.

Lead Data Engineer

• Lead the most complex and high-impact data architecture initiatives across clients, business areas, domains, platforms, or strategic programs.
• Define data architecture strategies that connect business goals, domain structures, semantic logic, platform realities, governance expectations, and downstream consumption needs.
• Serve as a senior advisor to internal and client stakeholders on architecture maturity, semantic structure, governance direction, platform-aligned design, and long-term maintainability.
• Establish and refine best practices for conceptual, logical, and physical data modeling, business entity design, semantic consistency, metric and dimension alignment, metadata expectations, domain boundaries, and reusable architecture patterns.
• Guide the design of scalable semantic structures, business concept frameworks, taxonomy and ontology-informed models, governed access patterns, and reference architectures that improve trust and downstream usability.
• Translate ambiguous executive and stakeholder questions into clear architecture approaches, semantic frameworks, platform strategies, governance-aligned design patterns, and business-relevant recommendations.
• Lead architecture reviews and design authority discussions to identify risks, resolve ambiguity, strengthen standards alignment, and improve long-term structural quality.
• Assess and shape how architecture choices support reporting, analytics, data products, machine learning, AI workflows, retrieval patterns, and agentic systems across structured, semi-structured, and selected unstructured data use cases.
• Provide governance direction through standards, design reviews, architecture guardrails, and decision frameworks while keeping hands-on governance execution lighter than framework and review ownership.
• Synthesize architecture tradeoffs, semantic implications, platform constraints, and governance considerations into clear insights, strategic implications, and recommended actions.
• Influence cross-functional teams across DEPA, DSAI, and AIO to improve how data platforms, governed data products, semantic layers, analytics, and AI workflows work together.
• Review major architecture deliverables to ensure quality, clarity, consistency, rigor, and practical business value.
• Contribute to thought leadership, growth initiatives, proposal strategy, solution shaping, and new business efforts where senior architecture expertise is required.
• Help create, improve, and promote reusable frameworks, templates, standards, semantic models, reference architectures, design review patterns, and accelerators that strengthen delivery consistency across the practice.
• Mentor senior practitioners and help define what excellent data architecture practice looks like across the organization.
• Reinforce strong governance, privacy, security, and data-quality expectations across engagements and teams.

• Lead the design and delivery of enterprise AI applications, copilots, assistants, and agentic solutions.
• Define implementation strategies that align AI capabilities with business objectives and user needs.
• Guide teams in building scalable AI workflows, retrieval systems, orchestration layers, and tool-integrated experiences.
• Drive best practices across prompt engineering, retrieval-augmented generation (RAG), tool calling, context management, testing, observability, and evaluation.
• Partner with clients to understand business challenges and identify AI-powered opportunities.
• Translate ambiguous requirements into practical implementation approaches and technical roadmaps.
• Act as a trusted advisor during workshops, discovery sessions, and executive presentations.
• Collaborate closely with Data Scientists, Data Engineers, Architects, Analytics Engineers, Product teams, and business stakeholders.

• Design, build, and operate data pipelines that process terabytes of transactional data daily using Airflow/Composer and BigQuery
• Own end-to-end data models and transformations that power merchant analytics, operational reporting, and ML features
• Build and maintain embedded analytics infrastructure — the data products our merchants interact with directly
• Evolve our data platform on GCP, including BigQuery, Cloud SQL, AlloyDB, and CDC datastreams
• Improve data quality and reliability through testing, observability, alerting, and validation frameworks
• Own data lineage, metadata, and documentation, and help prepare our data layer for agentic and LLM-powered use cases with semantic clarity and standardized metric definitions
• Collaborate cross-functionally with product, ML, and GTM teams, and contribute to technical direction through design docs and architecture decisions

Data Engineer jobs in Canada

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Senior Data Engineer

Senior Architect, Data

Senior Data Engineer

Senior Data Engineer – 6 month contract

Data/AI Engineer

Senior Data Engineer, EIM

Senior Enterprise Data Architect

Data Engineer

Senior Software Engineer – Data Hub

Intermediate Data Engineer

Product Manager – Unstructured Data Platform

Senior Product Manager, Data Platform – Intelligence

AVP, Enterprise Data Architecture

Commissioning Program, Data Engineer

Data Engineer, Investments

Senior Data Engineer

Lead Data Engineer

Lead Data Engineer

Data Engineer

Senior Software Engineer, Data