Top Data Jobs in Ottawa

Design, build, and operate cloud-native, serverless ClickHouse database infrastructure. Develop and maintain Kubernetes operators, improve metrics pipelines, support data ingestion/transformation (Spark/Kafka), collaborate with core DB and data plane teams, and ensure scalable, highly available distributed systems with production on-call responsibilities.

Lead analytics for Internal Audit Claims: extract, transform, reconcile, and validate large claims datasets; build and maintain Power BI reports/dashboards; perform QA and tie-outs; support automation, bots/AI enablement, and executive reporting; mentor other analysts and partner with audit, IT, and business stakeholders to deliver repeatable, validated analytics.

Manage interfaces and eligibility files for group insurance: perform daily data loading, validation and updates, document procedures, communicate with clients and stakeholders, and recommend process improvements and automation.

Evaluate and label model-generated and human-written code outputs, debug and assess code/agent trajectories, prompt models for complex data science tasks, review visualizations, and report quality trends to improve model behavior.

Lead design, build, and operate scalable data platform and pipelines (ETL/reverse ETL). Own architecture for AWS/Snowflake data lake, optimize ingestion, enforce data quality and governance, introduce AI-driven engineering practices, mentor engineers, and drive cross-functional data projects from requirements through production.

Design, build, and maintain company-wide Snowflake data assets and dbt data models. Define metrics and dimensions, implement reliable pipelines, enable self-service analytics, collaborate with stakeholders, ensure data quality, and document solutions to support reporting, dashboards, and analysis.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

Analyze large datasets to identify trends and deliver actionable insights; design and monitor KPIs; build and maintain dashboards and reporting frameworks; conduct ad hoc analyses; ensure data quality and support reporting infrastructure in partnership with product and business teams.

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.

Lead BI reporting and analysis for Advertising Technologies: design dashboards, define ETL/data models, create KPIs, resolve data quality issues, deliver recurring and ad-hoc reports, automate reporting workflows, document data logic, and collaborate with stakeholders to communicate insights.

Remote role to design, build, and maintain scalable Azure-based data solutions for the Digital Regulatory Assurance System. Build data pipelines and analytics-ready platforms, enable reporting, regulatory compliance, governance, metadata management, security, and support future AI initiatives using Python, SQL, Databricks, Data Factory, Synapse and Git-based collaboration.

Proactively explore RevenueCat data to identify customer problems and opportunities. Translate ambiguous product questions into analyses and production-grade predictive/descriptive models. Partner with Product, Engineering, and Analytics to shape roadmaps, define experimentation and benchmarking approaches, deploy and iterate on models powering customer-facing features, and communicate insights across technical and non-technical audiences.

Founding Data Scientist for RC Capital responsible for building underwriting, fraud detection, anomaly detection, and lead-qualification models; integrating risk signals into product flows; deploying and monitoring production models; analyzing cross-platform revenue data; and defining DS strategy to scale fintech products.

Abstract and enter clinical data from health records, ensure data quality and confidentiality, use EHR/practice management systems and Microsoft Office, troubleshoot basic access/VPN/MFA issues, and collaborate with clinical teams to support operations.

Design and own end-to-end data quality systems for managed AI data studies: define rubrics, sampling plans, automated checks, launch gates, drift detection, dashboards, and calibration. Investigate integrity issues, run root-cause analysis, build automation in Python/SQL, and partner with Product, Engineering, and Operations to embed quality controls, train reviewers, and scale repeatable quality playbooks across programs.

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.

Lead design, development, and implementation of data integration solutions for Oracle EPM Cloud (Planning, FCC, Reconciliation). Build ETL processes, configure DE/DM/OIC, perform data mapping/validation/testing, provide technical support and training, lead project teams, and recommend optimizations to ensure data accuracy and integration performance.

Design, build, and optimize Databricks-based data and ML platforms. Own ETL pipelines, Delta Lake lakehouse, MLflow model lifecycle, MLOps, cluster optimization, RAG/LLM solutions, BI integration, and data governance with Unity Catalog.

The Data Scientist will develop data labeling strategies, optimize datasets for LLMs, analyze data quality, and collaborate with engineering on data processing pipelines.

Senior Data Engineer responsible for designing and validating data solutions, consolidating disparate data sources, documenting functional specifications, reviewing data models, supporting production deployments, investigating data quality issues, and collaborating with stakeholders and technology leads to ensure data completeness and lineage.

Design, develop, and maintain data engineering solutions across the SDLC using Python (primary) and Scala (secondary). Build distributed data pipelines on Hadoop/Spark ecosystems, extend data processing platforms (Spark SQL, Hive, Starburst), manage CI/CD, and deploy on Kubernetes/OpenShift. Collaborate with stakeholders in Agile teams and ensure maintainable, reusable code.

Build and maintain responsive Angular UI components, collaborate with UX and backend teams, implement REST APIs and Python backend scripts, integrate basic ML models into data workflows, and participate in the full SDLC within Agile teams while ensuring code quality and performance.

Design, develop, test, and deploy high-performance Apache Spark/Scala data processing applications and ETL pipelines on Cloudera (CDH). Optimize Spark and platform performance, build data pipelines using HDFS, Hive, Impala, HBase, and Kafka, ensure data integrity and security, collaborate with data scientists and analysts, and implement version control and CI/CD for deployments.

Design and architect large-scale distributed big data solutions and ETL pipelines using Java, Scala, and Spark. Build and optimize Spark applications on Cloudera CDH (HDFS, Hive, Impala, HBase, Kafka). Ensure data integrity, troubleshoot performance issues, and implement version control and CI/CD for Spark workloads while collaborating with cross-functional teams.

Design and architect large-scale distributed Big Data solutions using Java, Spark, and Hadoop/Cloudera. Lead global teams to build high-volume data processing and analytics pipelines, ensure robust testing (JUnit), and apply scripting for automation and data analysis.