Top Data Jobs in Ottawa

Build and deploy end-to-end statistical and machine learning solutions for clients and internal delivery teams. Responsibilities include scoping ambiguous problems, analyzing operational challenges, prototyping AI workflows, translating messy data and real-world constraints into scalable solutions, iterating with stakeholders, and developing reusable tooling and infrastructure. The role combines hands-on data science with technical consulting and requires communication with technical and non-technical stakeholders.

Lead end-to-end data science and machine learning projects, from scoping ambiguous client problems through production deployment. Build and evaluate statistical and ML models, analyze large datasets, run experiments, collaborate with engineers, create reusable tooling, communicate findings to diverse stakeholders, and mentor data scientists. The role combines hands-on data science with technical consulting and requires occasional customer on-call support.

Leads Data Quality Analysts supporting AI model evaluation and improvement. Owns quality across workflows through audits, calibrations, feedback loops, trend analysis, and stakeholder alignment. Translates evolving guidelines into team direction, manages operational performance, supports hiring, drives process and tooling improvements, handles complex decisions, and coaches team members. The role balances quality, delivery, resource management, and continuous improvement in a human-in-the-loop AI environment.

Build and own production-grade data pipelines, source connectors, warehouse transformations, curated data models, reconciliation, and lineage for an AI-native finance platform. Develop dbt projects, orchestrate reliable and rerunnable workflows with Airflow or Dagster, tune warehouse performance, integrate messy enterprise systems, and ensure data correctness, auditability, and tenant isolation. Collaborate with backend and AI teams, establish data contracts, review work, and mentor engineers.

Design and build scalable backend systems, APIs, data models, event-driven services, experimentation platforms, and personalization systems. Partner with Product, Data Science, Analytics, and Design teams to support customer acquisition and engagement. Establish secure, observable, high-performance engineering practices, mentor junior engineers, and participate in monitoring, on-call, and operational support.

Coordinate customer onboarding, training, support, and retention workflows. Follow up on customer issues, align Sales, Accounting, Software, integrations, and licensing teams, and drive commitments through resolution. Build and improve operational processes, track projects, dependencies, blockers, and next steps, identify improvement opportunities from feedback, and provide clear updates across teams.

Analyze product usage and telemetry data to identify trends, opportunities, and product improvements. Partner with Product Analytics, Data Science, and product teams to define success metrics and measurement plans. Build dashboards, reports, and self-service analyses using SQL and analytics tools, validate telemetry and data quality, and communicate findings through written analyses, presentations, and recommendations.

Leads North American indirect tax requirements and global tax data governance for an SAP S/4HANA transformation. Oversees U.S. and Canadian tax processes, tax determination, reporting, reconciliations, data standards, tax technology integrations, testing, deployment, audit readiness, and compliance. Partners across Tax, Finance, IT, Master Data, Analytics, Procurement, Supply Chain, and Shared Services to improve tax data quality and establish scalable global operating models.

Lead development and evolution of AlphaSense's financial data taxonomy and generation rules. Serve as subject-matter expert, align cross-functional teams, define requirements for new datasets, ensure AI/LLM compatibility, maintain documentation, and build scalable frameworks to expand and maintain high-quality structured financial data for capital markets users.

Conducts clinical data analysis, audit coordination, regulatory reporting, workflow documentation, and corrective action planning for clinical operations. Reviews HEDIS, URAC, and NCQA compliance data, develops performance metrics and reports, supports audits and staffing analysis, researches healthcare regulations, and recommends process improvements. The role also assists with clinical staff training, business requirements, operational documentation, and supervisory coverage.

Leads partner-led and enterprise sales growth, primarily through AWS and Microsoft ecosystems. Builds strategic partnerships, develops enterprise accounts, drives end-to-end sales cycles, expands SMB sales processes, positions cloud and digital engineering capabilities, and manages pipeline forecasting. The role also helps shape sales strategy, organization, and scalable go-to-market processes.

Leads a team of application development engineers delivering complex enterprise software solutions. Oversees project scope, schedules, budgets, vendors, risks, security, service transitions, prototypes, staffing, performance management, and technical development. Partners with architects, data specialists, Security, and SRE teams while ensuring application quality, stability, and policy compliance. Requires strong leadership, communication, coaching, application architecture, DevOps, Agile, database, and data governance expertise.

Owns biological knowledge representation through LinkML and Biolink ontology engineering, semantic mappings, metadata harmonization, and schema governance. The role administers agentic curation and MCP tooling, develops LLM-driven extraction and validation workflows, and establishes automated quality controls, provenance, CI/CD, and evaluation standards. This senior individual contributor translates biological research requirements into canonical data models supporting genomics, single-cell, screening, imaging, and prior-knowledge datasets.

Own and scale a multi-cloud data platform across AWS and Azure. Manage Apache Airflow, Kubernetes, Terraform, Helm, ArgoCD, CI/CD, observability, big data infrastructure, governance tooling, and FinOps initiatives. Provide end-to-end architectural and operational ownership, improve developer tooling, define platform roadmaps, manage incidents, enforce engineering standards, and mentor data platform engineers.

Lead architecture and hands-on engineering of Pantheons data platform: extend ingest, curation, and retention services; build ML/LLM-ready data infrastructure; mentor engineers; operate in a DevOps model; ensure data governance, observability, reliability, and cross-functional partnership.

Own revenue-focused data science from opportunity sizing through production ML. Investigate and measure product changes, build and operate batch and online ML systems for personalization, experimentation, and automation, and partner cross-functionally to integrate models into user-facing products. Establish monitoring, lineage, reproducibility, and security practices; improve data pipelines; mentor data scientists; and define ML development best practices while using AI tools to accelerate delivery.

In this role, you will define and execute the go-to-market strategy, drive growth, and lead customer success efforts at Reflow, a workplace intelligence platform.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, workflow automation, evaluation, and observability capabilities. Work hands-on with Python, SQL, dbt, lakehouse data, cloud infrastructure, model selection, and inference-cost optimization. Establish reusable engineering patterns and mentor teams through high-quality code and system design.

Own the strategy, roadmap, and delivery of Coalition’s scanning and cyber-risk data products. Partner with Engineering, Data, Security, Underwriting, Actuarial, Design, and customer teams to improve asset discovery, data quality, accuracy, reliability, scalability, and usability. Define metrics, prioritize initiatives, translate technical challenges into product requirements, guide discovery through launch, and develop APIs and experiences that help stakeholders understand and act on cyber-risk intelligence.

Leads advanced data analytics for Tribal healthcare initiatives, including data resource inventories, quality assessment, linkage analysis, predictive and prescriptive modeling, reporting, dashboards, and large-scale analytics projects. Establishes data quality frameworks, supports federal compliance, coordinates cross-functional initiatives, and mentors junior analysts. Uses Python, R, SQL, statistical methods, machine learning, and data modeling to produce actionable insights and maintain data integrity.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Own end-to-end financial data analysis, including SQL and dbt data modeling, Looker dashboard development, data quality validation, reconciliation, and stakeholder-facing insights. Partner with Finance and Payments to support reporting, planning, revenue analysis, cost insights, and transaction-level analytics. Translate ambiguous business needs into reliable technical solutions while improving documentation, QA, modeling, and data architecture practices.

Business-focused consultant connecting divisions with enterprise data, analytics, and AI functions. Facilitates discovery workshops, identifies technology opportunities, translates business challenges into AI and data use cases, and develops decision-ready business cases covering benefits, costs, risks, dependencies, and value realization. Partners with business, finance, technology, and delivery teams to guide opportunities through prioritization, delivery, adoption, and measurable outcomes.

Build production AI harness systems for Kinaxis’s Databricks-based enterprise Data Fabric. Responsibilities include developing agent execution frameworks, integrating LLMs with governed data, implementing tool calling and workflow controls, enforcing multi-tenant isolation, and delivering APIs, SDKs, libraries, templates, and operational runbooks. The role requires distributed-systems engineering, cloud data-platform experience, secure multi-tenant design, and practical expertise in agent orchestration, retrieval, evaluation, guardrails, metadata, lineage, and governance.

Own growth marketing data science for a key domain, developing optimization algorithms, attribution and incrementality models, experimentation strategies, analytics pipelines, dashboards, and strategic insights. Partner with marketing, product, engineering, data engineers, and ad platforms to improve customer acquisition, campaign performance, and marketing ROI. Serve as the end-to-end data domain owner and translate complex analyses into business decisions.