Top Data Jobs in Ottawa

Maintains project and amendment setup in the accounting system, processes change orders and fee increases, supports contract administration and audits, prepares project reports, identifies discrepancies and risks, and collaborates with project managers and Finance and Accounting teams.

Lead Cohere’s Data Foundation pillar, managing a team responsible for the full data lifecycle. Own the roadmap, BigQuery warehouse, Airflow and Cloud Run orchestration, dbt modeling, Looker and API serving, Terraform infrastructure, governance, access controls, reliability, incident response, and data freshness SLAs. Lead development of an agentic analytics platform and evaluation framework, establish company-wide analytics standards, and partner with product, engineering, security, legal, and business teams.

Develop and maintain large-scale data and machine learning applications using Scala, Python, Spark, AWS, and modern DevOps practices. Responsibilities include designing data pipelines, deploying and monitoring machine learning models, optimizing distributed systems, supporting the full software lifecycle, documenting processes, and collaborating with data science, Data Ops, DevOps, and product teams in an Agile environment.

CommBank is seeking Indigenous job seekers for various roles across its sectors, offering traineeships, internships, and direct employment opportunities with support from an Indigenous Careers team.

Lead the architecture and delivery of GitLab’s data products, including near-real-time data ingestion, knowledge graph integrations, data marketplaces, observability, and data-sharing APIs. Translate ambiguous 0-to-1 initiatives into roadmaps, resolve cross-team dependencies, ensure operational readiness across deployment surfaces, and mentor engineers. The role requires deep full-stack expertise, technical leadership, and experience with AI-powered orchestration and multi-tenant SaaS systems.

Leads the enterprise data and integration strategy, architecture, governance, engineering standards, and technology roadmap. Builds and mentors a Data Engineering team, establishes scalable integration and data management practices, oversees platforms and vendors, and enables analytics, AI, automation, and digital transformation. The role partners with business, technology, cybersecurity, and architecture leaders to standardize data capabilities, improve quality and security, reduce technical debt, and deliver sustainable enterprise solutions.

Leads the enterprise data and integration capability, developing data strategy, governance frameworks, architecture standards, integration services, technology roadmaps, and data engineering practices. Oversees platforms, APIs, data pipelines, metadata, lineage, data quality, analytics, AI readiness, and modernization initiatives. Builds and mentors a Data Engineering team while partnering with business, technology, cybersecurity, infrastructure, architecture, and external stakeholders to establish secure, scalable, governed enterprise data solutions.

Partner with sales teams to win strategic data security opportunities. Deliver technical presentations, gather customer requirements, lead product evaluations, configure solutions, and design test plans. The role requires extensive pre-sales experience, security and networking expertise, data protection knowledge, AI fluency, and collaboration across matrixed go-to-market and partner teams.

Leads client delivery programs across multiple accounts, serving as the primary client contact, communicating status and risks, maintaining accurate systems, and coordinating projects through resolution. Builds partner relationships, identifies account expansion opportunities, develops account plans, leads business reviews, and connects client needs with research teams. Provides leadership reporting, improves operational templates and handoffs, and ensures clients and internal stakeholders remain aligned.

Leads marketing and loyalty analytics strategy, measurement frameworks, forecasting, campaign reporting, experimentation, and lifecycle analysis. Partners with Marketing and Finance to connect performance metrics to business goals, automate reporting, and generate actionable insights. Builds churn, retention, segmentation, and LTV models while evaluating paid acquisition and customer growth opportunities using SQL, Python, BI tools, marketing platforms, and modern AI-enabled workflows.

Build Databricks data pipelines and reliable data models, then design Power BI dashboards and reports using DAX, row-level security, and efficient modeling. Collaborate with clients, developers, product teams, and IT stakeholders to gather requirements, deploy solutions, improve data quality, and optimize dashboard performance. The role supports nuclear utility and reactor project clients in a hybrid environment with regular client-site travel.

Design, develop, and own data pipelines, models, data marts, and data products supporting Product, Data Science, and GTM teams. Build data infrastructure and services using Spark, Scala, Java, SQL, Python, and Airflow; maintain pipeline and application SLAs; investigate data quality issues; leverage LLMs and agents; and collaborate cross-functionally to architect shared datasets and business reporting solutions.

Supports pharma, biotech, and MedTech data teams through web-based information sourcing, content review, data analysis, and quality assurance. The role includes spreadsheet work, prompt engineering for large language models, basic coding, and projects expanding platform data. This is an eight-month rotational co-op placement across two brands, focused primarily on data quality and sourcing rather than data engineering.

Own data science for customer-facing product features, including LTV prediction, experimentation, benchmarking, and forecasting. Proactively analyze data to identify customer problems and product opportunities, build and deploy production-grade models, and influence product roadmaps. Partner with Product, Engineering, and Analytics, communicate findings to varied audiences, iterate on shipped features, and lead new data-driven initiatives.

Develop and maintain scalable big data pipelines using Python, PySpark, Spark SQL, and ETL workflows. Process structured and unstructured data across distributed systems and cloud or on-premises environments. Optimize Spark jobs, maintain unit tests, monitor pipeline reliability, troubleshoot issues, and collaborate with engineers, analysts, and stakeholders to deliver efficient data solutions.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Processes and maintains investment market, master, and corporate-action data in SAP FAM to support accurate investment accounting. The role documents procedures, responds to finance and investment operations inquiries, supports projects, and improves data-control processes and systems. Candidates should understand investment products, accounting treatments, control environments, investment operations systems, and market-data platforms.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

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.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, experiments, and recommendations that drive decisions and measurable outcomes. Partner with cross-functional stakeholders, communicate insights clearly, and independently solve ambiguous problems using SQL and Python.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires strong analytical skills, independent data work, persuasive communication, and ownership of ambiguous problems with measurable business impact.

Own end-to-end data science and analytics for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires strong SQL or Python skills, first-principles problem solving, independent data interpretation, persuasive communication, and ownership of ambiguous business problems.

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independent data work, strong analytical thinking, clear communication, and end-to-end ownership of ambiguous, high-impact initiatives.

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.