Top Data Jobs in Ottawa

Reconstructs and redesigns data-driven presentations in Microsoft PowerPoint, creating visualizations and analytics reports that communicate complex insights clearly. Reviews and edits content for accuracy, flow, and narrative impact; applies data analysis and statistical reasoning; and refines slides based on feedback. The role supports AI training projects through high-quality presentation and analytical deliverables.

Designs and implements enterprise data architecture solutions, including logical and physical data models, source-to-target mappings, data flows, and database strategies. Reviews source systems, validates data using advanced SQL, governs models against enterprise standards, and collaborates with business, architecture, and development teams. The role requires extensive data modeling, RDBMS, SQL, analysis, and enterprise architecture experience, with Python, Azure Data Lakehouse, Parquet, Delta Lake, and CASE tool experience preferred.

Build infrastructure for reproducible robot training and evaluation datasets, including multimodal ingestion, temporal alignment, provenance, quality controls, storage, versioning, replay, and reliable delivery. Own schemas, lineage, observability, failure recovery, safe backfills, and dataset reproducibility across video, sensor, command, intervention, outcome, and machine-health data.

Own and scale application security across Redpanda’s C++ engine, Go control plane, Console, and Rust/Go SDKs. Responsibilities include threat modeling, secure design reviews, SAST/SCA/DAST, fuzzing, secure code review, PSIRT and vulnerability disclosure, software supply-chain security, security release gates, and security champions. The role partners closely with engineering to improve secure development practices, address vulnerabilities, and protect authentication, authorization, encryption, audit logging, and multi-tenant isolation.

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.

The Data Analyst develops and maintains Power BI dashboards, datasets, data models, SQL and DAX solutions, and data pipelines. The role supports AWS cloud analytics integration, data quality, troubleshooting, governance, documentation, and workspace administration. It coordinates data engineers, translates business requirements into technical deliverables, collaborates with stakeholders, and communicates findings to nontechnical audiences while supporting agile delivery and data privacy compliance.

Analyze full-funnel B2B marketing performance using dashboards, SQL, attribution modeling, and CRM data. Build Tableau or Looker Studio reporting, query Redshift data, and apply Python, AI APIs, prompt engineering, and automation to streamline data processing and performance summaries. Partner with campaign, web, and sales operations teams to improve tagging, KPI consistency, lead velocity, and conversion insights.

Design, build, and scale backend data services, APIs, microservices, distributed systems, and high-volume data pipelines. Optimize PostgreSQL and Oracle databases, improve reliability and performance across regions, diagnose complex production issues, and mentor engineers. Partner with DBA and SRE teams while providing hands-on technical leadership and owning services end to end.

Analyze large datasets to produce dashboards, metrics, and insights supporting product, care, and business decisions. Build BI infrastructure, run A/B and multivariate tests, perform ad-hoc analyses and visualizations, and collaborate with stakeholders and data scientists to inform strategy and operations.

Leads the enterprise data engineering capability, including architecture, integration patterns, cloud data platforms, scalable pipelines, APIs, data governance, and real-time processing. Establishes standards and roadmaps across Azure, Snowflake, and MuleSoft while enabling business intelligence, automation, machine learning, RAG, vector search, and AI initiatives. Collaborates with stakeholders and technical teams to deliver governed, observable, high-quality enterprise data solutions.

Leads enterprise data engineering and AI platform strategy, including Azure, Snowflake, and MuleSoft architectures; scalable pipelines, APIs, data lakes, warehouses, governance, integration, real-time processing, and AI capabilities such as RAG and vector search. Partners with stakeholders to deliver trusted data products supporting analytics, automation, reporting, machine learning, and enterprise AI initiatives.

Supports client engagements involving data de-identification and re-identification risk measurement, particularly for healthcare data. Responsibilities include validating and preparing datasets, conducting qualitative and quantitative risk analyses, documenting findings in client reports, preparing presentations, supporting solution delivery, coaching clients, and developing expertise in statistical disclosure control, privacy regulations, and de-identification standards.

Develop and optimize Supabase’s Data API based on PostgREST. Responsibilities include improving the PostgreSQL driver and performance, developing features, fixing bugs, resolving incidents and escalated support issues, improving observability, and debugging integrations across the Supabase platform. The role requires strong Haskell development, PostgreSQL and SQL expertise, Linux debugging, open-source contribution, and technical user support.

Develop and optimize secure networking software for Ribbon’s next-generation products across cloud, server, and custom hardware platforms. Responsibilities include data-plane acceleration using network processors, debugging customer issues, collaborating with system testing, and measuring product performance. The role requires strong C and embedded programming experience, networking expertise, Linux and kernel knowledge, and familiarity with technologies such as DPDK, VPP, SmartNICs, Kubernetes networking interfaces, tunneling, and eBPF.

Analyze the customer lifecycle across activation, engagement, retention, expansion, and renewal for a product-led SaaS business. Partner with Customer Success, Product, Growth, RevOps, and Data Engineering to develop analytical frameworks, identify growth opportunities, evaluate experiments, and provide actionable recommendations to senior leaders. Build data foundations and standards supporting digital success, AI, automation, forecasting, anomaly detection, and generative BI.

Conduct independent research on machine learning, knowledge representation, formal methods, AI agents, and trustworthy AI systems. Develop algorithms, models, prototypes, benchmarks, and rigorous experiments for enterprise-scale applications. Collaborate with scientists, engineers, and domain experts to translate operational constraints into deployable research systems. Evaluate reliability, robustness, privacy, interpretability, latency, cost, and process outcomes, while communicating findings through publications and internal or external presentations.

Work full time on a defined data science and analytics project using production robot, retail, and operational data. Responsibilities include SQL and Python analysis, BigQuery and dbt workflows, dimensional modeling, dashboard development, data-quality validation, analytical insights, and collaboration with technical and business stakeholders. Interns present their project and results at the end of the program.

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.

Build data and evaluation systems for Glean’s AI products, including benchmark datasets, scalable eval infrastructure, agent observability, LLM-based verifiers, CI and production evaluation pipelines, and enterprise simulation environments. Analyze traces and failures, measure quality with statistical rigor, and translate findings into product improvements. Partner with research, engineering, product, and go-to-market teams to improve agent performance while balancing quality, latency, and cost.

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.

Designs, builds, and maintains scalable batch and streaming data pipelines across AWS and GCP. Develops ETL/ELT workflows using Python, PySpark, and advanced SQL; optimizes performance, storage, and query execution; implements data quality and governance controls; and supports data lakes, warehouses, and ML workflows. Collaborates with data scientists and ML engineers on MLOps automation and production deployments while maintaining CI/CD, Docker, infrastructure standards, and technical documentation.

Leads data integration development using Azure, Hadoop, Spark, Scala, Java, SQL, and relational databases. Responsibilities include data modeling, analytics, solution design, requirements analysis, development, maintenance, and quality delivery in an agile, client-focused environment. The role requires strong problem-solving, interpersonal, communication, and technical skills, along with adaptability, motivation, and the ability to learn quickly.

Leads hands-on data integration and engineering initiatives using Azure Databricks, Azure Data Factory, PySpark, Python, and SQL. Modernizes legacy on-premises systems into cloud-native architectures, implements Medallion Architecture and data warehousing, improves pipeline reliability and compute efficiency, develops CI/CD workflows, and mentors engineering teams delivering secure, actionable analytics for banking environments.

Leads hands-on data integration and engineering initiatives using Azure Databricks, Azure Data Factory, PySpark, Python, and SQL. Modernizes legacy on-premises systems into cloud-native architectures, implements Medallion Architecture and data warehousing, improves pipeline reliability and compute efficiency, establishes CI/CD workflows, supports banking-grade security, and mentors engineering teams delivering actionable analytics.

Lead real-time data integration using Azure Databricks, PySpark, Kafka, ADF, and ADLS. Build and optimize medallion-style ETL pipelines processing over 10 million daily records. Improve transformation efficiency, data quality, validation, and downstream reliability by identifying upstream data issues, implementing root-cause fixes, and collaborating with stakeholders.