Top Data Jobs in Ottawa

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.

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.

Supports public health data extraction, management, quality assurance, cleaning, validation, harmonization, documentation, and statistical analysis. Develops analytic datasets and performs qualitative, quantitative, and mixed-methods evaluations of program implementation and outcomes. Prepares technical memoranda, reports, tables, figures, and presentations, and communicates findings to program stakeholders. Requires knowledge of public health research, evaluation methods, government procedures, CDC administrative systems, and Microsoft Office.

Leads the technical vision and delivery of an enterprise agentic AI program. Oversees architecture, evaluation, deployment, MLOps, reliability, security, and scalability across multiple workstreams. Partners with executives and client stakeholders on roadmaps and high-stakes decisions, aligns data science and engineering teams, establishes technical standards, and mentors senior technical leaders. Requires deep experience in AI/ML, LLMs, agentic systems, AWS, distributed systems, evaluation, and production-scale software engineering.

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.

Lead the architecture and hands-on engineering of Cloudbeds’ Snowflake and dbt data platform. Own data modeling, warehouse design, ingestion, orchestration, DataOps, CI/CD, governance, observability, performance, cost optimization, and AI/ML readiness. Mentor analytical engineers, evaluate emerging tools, establish data quality and security standards, and collaborate with engineering, infrastructure, data science, and business stakeholders in a fully remote environment.

Leads complex data science and analytics initiatives, develops and validates predictive models, evaluates diverse data sources, and translates findings into actionable business insights. Determines technical approaches, supports risk mitigation and decision-making, and provides technical leadership and mentorship to analytics professionals. Collaborates with stakeholders while promoting quality, consistency, and data-driven solutions.

Design, build, and maintain scalable data ingestion pipelines, databases and data lakes for credit and lending datasets. Ensure accurate data mappings, monitoring and alerting, translate stakeholder requirements into analytical data flows, and integrate APIs to deliver reliable, high-quality data products.

Drive strategic federal sales growth across DISA 4th Estate and Intelligence Community accounts. Responsibilities include pipeline development, account planning, opportunity and win strategy, stakeholder relationship development, acquisition navigation, RFI and solicitation support, CRM management, forecasting, and translating technology solutions into mission outcomes. The role requires expertise in long-cycle federal sales, procurement processes, secure environments, and technology solutions.

Own the vision, strategy, roadmap, development, commercialization, and lifecycle of commercial data products. Identify customer and market opportunities, translate needs into product requirements, and collaborate with data engineering, data science, analytics, software, sales, and marketing teams. Lead discovery, launches, monetization, adoption, optimization, and measurement of business outcomes across revenue, operations, customer experience, and decision-making.

Evaluate AI model outputs, audit data and model behavior, identify edge cases and failure modes, apply and improve evaluation guidelines, communicate performance trends, participate in calibration discussions, and document actionable findings. The role requires native-level Tamil, strong English and writing skills, critical judgment, attention to detail, and familiarity with GenAI safety considerations. This is a full-time, one-year, 100% onsite contract position.

Evaluate AI model outputs, conduct audit-based reviews, identify patterns and failure modes, apply and improve evaluation guidelines, and document actionable findings. The role requires native-level Marathi, strong English, structured writing, GenAI safety knowledge, critical thinking, and comfort making judgment calls in ambiguous situations. Associates collaborate with leads and stakeholders, participate in calibrations, and help refine quality standards. This is a full-time, one-year, 100% onsite contract role in specified U.S. cities.

Leads integration of two major financial data product launches into the AlphaSense platform. Defines zero-to-one product lifecycles, user stories, milestones, roadmaps, and PRDs; translates investment research workflows into data architectures; manages resources and delivery across teams; mentors product managers; validates solutions with customers and engineering; and drives competitive intelligence, prioritization, and cross-functional alignment.