Top Data Jobs in Ottawa

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 the design, development, and operation of AOF’s data platform, including data lakes, warehouses, ingestion, orchestration, governance, reporting, and AI capabilities. Builds internal and customer-facing products using Python, Go, TypeScript, Terraform, and AWS. Drives architecture, security, CI/CD, cloud migration, regulatory reporting, and responsible AI deployment while partnering across business teams. Initially serves as a hands-on individual contributor and later grows an engineering team.

Leads end-to-end delivery of investment data solutions, translating business objectives into requirements, data mappings, lineage, testing, and implementation plans. Analyzes and reconciles complex datasets using SQL and Python, supports data governance and quality, coordinates UAT and defect resolution, and develops Power BI reports and dashboards. Partners with investment, operations, risk, compliance, technology, vendors, and senior stakeholders to deliver reliable analytics and reporting solutions.

The Investment Technology Business Analyst will deliver front-office technology and data implementations for asset and wealth management clients. Responsibilities include business analysis, SQL-based data work, data modeling, governance and quality management, cloud data platform implementation, and expertise across investment processes such as risk, performance, compliance, reporting, derivatives, operations, or investment data. The role also involves leading small to medium-sized consulting teams, developing technical solutions, managing client relationships, and traveling as needed.

Leads pre-sales discovery, solution architecture, strategic advisory, scoping, SOW development, RFP responses, and deal strategy for large-scale data, AI, and cloud transformations. Designs GCP-based data, ML, GenAI, and agentic solutions; facilitates executive workshops; shapes industry-specific offerings; supports opportunities exceeding $1 million; and serves as a trusted technical advisor to C-suite and architecture stakeholders.

Drives enterprise client success and operational excellence by scoping requirements, designing scalable processes, coordinating cross-functional delivery, analyzing performance, optimizing quality, and supporting analytics initiatives. Acts as a bridge between enterprise clients, a global talent network, and internal product, engineering, and operations teams. The role also includes mentoring operational staff, managing stakeholder relationships, and helping build new marketplace capabilities.

Analyze user acquisition, advertising revenue, game portfolio, and finance data. Build dashboards and automated reports, identify trends, support campaign performance analysis, improve data workflows, and maintain reporting accuracy. The role requires SQL, Python or R, quantitative modeling, cohort analysis, LTV forecasting, and strong communication with cross-functional stakeholders. Familiarity with experimentation, AI-assisted coding, modern databases, big data, and performance marketing is preferred.

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.

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.

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.

Analyzes financial and operational data to support decision-making across Customer Operations and Finance. Responsibilities include maintaining IFRS 9 expected credit loss models, writing SQL queries, developing financial and trend analyses, managing data warehouses, creating Power BI dashboards, improving management reporting, supporting audits, and automating processes with scripting or programming. The role collaborates with finance, analytics, and cross-functional stakeholders to deliver accurate insights and reporting.

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.