Top Data Jobs in Ottawa

Leads HR data, people analytics, workforce insights, Power BI dashboards, automation, AI-enabled solutions, data governance, HR technology roadmaps, and digital transformation. Partners with HR, IT, business leaders, and vendors to improve decision-making, optimize processes, support adoption, and develop future-state HR operating models.

Leads HR data analytics and digital transformation by developing Power BI dashboards, workforce insights, automation, AI-enabled solutions, data governance, and HR technology roadmaps. Partners with HR, IT, business leaders, and vendors to improve processes, guide decisions, support adoption, and deliver sustainable change across HR operations.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Maintain accurate manufacturing master data across ERP, PLM, MES, and analytics systems. Audit item records, identify discrepancies, investigate root causes, document data structures and governance rules, and support automated quality checks, dashboards, ERP testing, and UAT. Collaborate with Manufacturing, Engineering, Supply Chain, IS/ERP, and data-entry teams to resolve routing, work-order, and status issues while improving data processes and system reliability.

Sets the technical vision for data science, leads complex credit risk, pricing, and loss forecasting models from conception through production, and architects scalable machine learning pipelines. Partners with senior stakeholders to shape strategy, establishes modeling and validation standards, mentors technical staff, and communicates recommendations to executives. The role requires deep fintech lending experience, advanced statistical and machine learning expertise, and hands-on production model deployment.

Designs and demonstrates secure, scalable Azure database and analytics solutions for enterprise customers. Leads technical sales engagements, proofs of concept, hackathons, architecture workshops, migrations, and production deployments. Advises platform leaders, resolves technical blockers, collaborates with engineering, and develops expertise across Microsoft Fabric, Azure Databases, Databricks, Purview, on-premises data warehouses, Hadoop, and competing analytics platforms.

Develop customer-specific AI solutions for consequential business decisions. Investigate internal and public information, evaluate evidence, identify uncertainty, and design reliable AI workflows. Work directly with customers to understand needs, prototype solutions, test them with users, and improve their usefulness. Translate successful workflows into repeatable product capabilities while partnering with engineers on integrations and missing technical functionality.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Develop and enhance Grafana data source plugins integrating third-party services and databases. Support plugin reliability and operations in Grafana Cloud, integrate data sources with agentic workflows, improve engineering processes, assist with customer escalations, and collaborate across the Grafana Division. The role requires strong Golang and TypeScript skills, Kubernetes and microservices experience, robust software development practices, and participation in on-call and SRE operations.

Develop and maintain ETL processes, SQL Server databases, SSAS data models, SSRS reports, and Power BI dashboards. Build unified data models across ERP systems, improve BI performance, establish development standards, and translate cross-functional business requirements into scalable data and integration solutions. The role also supports modernization of Sofina’s data environment and strategic reporting for finance, sales, supply chain, and other stakeholders.

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.

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.