Top Data Jobs in Ottawa

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.

Leads Databricks’ Public Sector enterprise sales organization in Ottawa, managing account executives, quotas, territory planning, forecasting, hiring, coaching, and revenue growth. Builds executive customer and partner relationships, drives consumption- and commitment-based sales, ensures forecast accuracy, and collaborates with cross-functional leaders to execute strategic growth plans. Requires leadership experience in enterprise software sales across data, AI, cloud, or SaaS, preferably serving public-sector customers.

Lead the design and implementation of Agiloft’s contract data model, including scalable pipelines, data warehouses, analytics layers, governance, monitoring, and quality controls. Drive architecture decisions, schema redesigns, and migrations in a multi-tenant SaaS environment while collaborating with Product, Engineering, Data Science, ML, and customer-facing teams. Provide technical mentorship and guide multi-quarter data initiatives supporting AI, analytics, and contract intelligence.

Own supplier data quality through profiling, sampling, source linking, issue quantification, and quality monitoring. Partner with data engineering, product, and cross-functional teams to translate findings into technical improvements and prioritize opportunities. Build dashboards and reports to communicate data health, support automated quality gates, and apply customer insights to data and product improvements.

Build and maintain financial data infrastructure supporting accounting and reporting. Translate finance requirements into scalable data solutions, ingest and transform ERP data, develop Fabric lakehouses and warehouses, maintain Power BI semantic models, and consolidate general ledger, accounts payable, accounts receivable, and asset-management data. Partner with accounting, finance, analytics, and architecture stakeholders to ensure data quality, governance, consistency, and availability.

Design and deliver large-scale data pipelines and cloud migration solutions using Azure Synapse Analytics. Build and optimize ETL/ELT workflows processing terabytes or petabytes of structured and unstructured data. Develop automation, monitoring, testing, storage, and compute tooling across Azure environments. Collaborate with infrastructure, engineering, DevOps, and cloud teams while mentoring engineers and leading large projects.

Own and operate the company’s Microsoft Fabric data platform and DOMO reporting environment. Build lakehouses, pipelines, models, monitoring, and governed datasets for analytics and AI agents. Develop MCP and API connectors with secure permissions and logging, improve architecture and control costs, and ensure data quality, lineage, privacy, and compliance. Work independently in production environments using Git, Bitbucket, and Azure DevOps while collaborating with technical and nontechnical teams.

Develop a strategic framework for identifying, tracking, ranking, and matching emerging film, television, and digital talent. Define data signals, scoring methods, segmentation, personalization, and matching logic based on audience fit, content fit, and campaign goals. Produce a talent-tracking criteria deck, translate entertainment campaign objectives into testable requirements, and collaborate with engineers to ensure scalability. The role also covers recommendation algorithms, ranking and retrieval systems, reinforcement learning, machine learning optimization, and performance measurement.

Perform precise 3D data annotation and labeling in the Cognic platform, follow project guidelines, complete paid training and certification, and maintain quality and productivity standards. The role is fully remote with a flexible schedule and requires reliable computer equipment, stable internet, strong English skills, and at least two years of 3D annotation experience.

Leads business intelligence and data analysis initiatives by examining scientific and operational datasets, developing KPI insights, mapping business and data processes, and conducting deep-dive analyses. Engages stakeholders, translates requirements into user stories, leads requirements workshops and UAT, validates data, creates process and data-flow diagrams, and collaborates with engineering, product, and business teams to deliver analytics solutions.

Develop machine learning models and detection rules for web security products. Analyze large datasets to distinguish legitimate from fraudulent traffic, improve detection accuracy, support production data workflows, troubleshoot distributed systems, and provide insights that guide product improvements and strategy.

Own backend data flows, schemas, migrations, APIs, event pipelines, customer integrations, bulk data tools, observability, and on-call operations. Build reliable TypeScript and Node.js services using PostgreSQL, Pub/Sub, BullMQ, Google Cloud, and Kubernetes. Ensure at-least-once delivery, idempotency, scalable queries, zero-downtime schema changes, and dependable movement of large image and detection datasets across services and customer systems.

Build and deploy large-scale LLM-based recommender systems from raw customer and catalog data through production inference. Responsibilities include feature and context engineering, distributed training on SageMaker HyperPod, LoRA/QLoRA fine-tuning, reinforcement learning, deep learning architecture, ranking, inference optimization, evaluation, privacy protection, and stakeholder communication with Expedia and AWS science teams.

Build and maintain MLOps infrastructure for listener data and machine learning systems. Responsibilities include developing CI/CD pipelines, optimizing Apache Spark and Databricks workloads, productionizing models, managing scalable APIs and batch systems, implementing testing and monitoring, and supporting reproducible, cost-efficient cloud and containerized environments.

Own the strategy, roadmap, requirements, delivery, and go-to-market activities for Atlas’s SIEM, log management, and security data capabilities. Lead customer discovery, competitive analysis, product prototyping, backlog prioritization, partner engagement, and KPI measurement. Collaborate with engineering, SOC, UX, customer success, deployments, sales, and marketing teams to deliver AI-led security products and services.

Lead architectural design and technical development for data center and mission critical projects. Produce and coordinate technical drawings from concept through construction documentation, ensure code and regulatory compliance, guide junior staff, collaborate with clients and project partners, and monitor project timelines, budgets, and deliverables.

Manage complex data center architecture projects from concept through construction and final handover. Responsibilities include developing schedules and budgets, coordinating architects, engineers, contractors, and clients, monitoring progress, managing risks, ensuring regulatory compliance, reviewing designs, conducting site visits, resolving issues, maintaining documentation, and delivering projects on time and within budget. The role also provides team leadership, stakeholder communication, quality oversight, and post-project evaluation.

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.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and trusted analytics foundations. Lead cross-functional data initiatives, translate ambiguous business needs into scalable solutions, improve reliability and quality, and mentor engineers. Build and monitor production LLM and agent systems, automate workflows with AI, and deliver high-quality Python and SQL while validating AI-generated code. Partner with Marketing, Sales, BI, Data Science, and R&D teams.

Own marketplace product data quality and discoverability across product information, taxonomy, content, images, compliance, and marketplace feeds. Maintain marketplace requirements, map PIM data, run quality checks, resolve feed errors and suppressions, and track products through launch and sales. Diagnose poor visibility or conversion and coordinate root-cause fixes with business and content teams. Support SEO, marketplace search ranking, structured data, and AI-driven commerce initiatives.

Owns enterprise data architecture across on-premises and cloud environments. Designs data models, integration patterns, platforms, governance standards, security controls, and migration roadmaps. Evaluates data technologies, supports Azure modernization, ensures compliance with CUI, NIST SP 800-171, and CMMC 2.0, and documents architecture decisions. Partners with technical, security, infrastructure, application, and business teams while mentoring engineers and analysts and communicating recommendations to executives.

Own and improve Life360’s Ads Data Engineering platform on Databricks. Build, operate, and optimize batch and streaming pipelines, Delta Lake tables, dbt models, semantic layers, and core metrics. Integrate advertising and business data, implement quality and governance controls, manage AWS infrastructure with Terraform, reduce compute costs, and mentor engineers. Partner with analysts, ad strategists, and sales teams to deliver trusted data products while developing AI-assisted engineering tools and workflows.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires independent analytical problem-solving, strong communication, and ownership of ambiguous initiatives that materially improve business outcomes.

Own end-to-end data science for a business area such as growth, product, AI, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independently structuring ambiguous problems, using SQL or Python, communicating findings clearly, and driving measurable business outcomes.

Designs challenging competitive data science and machine learning tasks, reproducible execution environments, reference solutions, automated tests, and evaluation criteria. Uses Python, statistical modeling, mathematical reasoning, and debugging to assess AI coding agents, identify data leakage and implementation flaws, validate solution correctness, and document methodologies. Requires a verifiable Kaggle Expert-level profile, strong practical programming skills, and the ability to independently test and explain technical solutions.