Top Data Jobs in Ottawa

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.

Analyze product, customer, risk, and support data to guide decisions across Block’s platforms. Own metrics, forecasting, staffing models, experiments, visualizations, and measurement frameworks. Evaluate risk models and policies for performance, bias, and drift; use AI tools and agent workflows to improve analytical execution. Partner with product, engineering, and risk stakeholders, lead technical standards, build reusable tooling, mentor data scientists, and contribute to hiring.

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.

Designs and delivers secure, scalable Azure cloud, data, analytics, database, and AI solutions for customers. Leads architecture, roadmaps, proofs of concept, production deployments, workload optimization, governance, resiliency, and security initiatives. Partners with technical and business stakeholders, supports customer adoption and Azure consumption, resolves technical blockers, contributes reusable deployment frameworks, and mentors others. Requires strong expertise in Microsoft Fabric, Azure Databricks, Purview, Azure databases, data engineering, governance, migration, and cloud architecture.

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.