Top Data Engineer Jobs in Ottawa

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.

The Software Engineer will manage data collection for model training operations, enhance cloud infrastructure, and collaborate with AI scientists to improve data quality and scale.

As a Data Analyst, you will analyze user behavior, create interactive dashboards, and collaborate with teams to provide actionable insights for product improvement.

The Guidewire Data Engineer will design schemas in Snowflake, develop ETL processes, optimize SQL queries, and integrate Guidewire with data platforms.

Design, build, and optimize cloud-based data platform components: ingest and transform varied datasets, create metadata-driven pipelines and data APIs, support AI/ML infrastructure, implement monitoring and metrics, and improve data processes to enable analytics and operational systems.

As an Intermediate Data Engineer, you will develop data infrastructure and products, collaborate with diverse teams, and enhance technical quality across the data team.

Lead design and implementation of a federated Lakehouse data platform, unify data silos, define governance/observability, enable secure AI/ML (RAG/vector readiness), mentor architects/engineers, and present roadmaps to executives.

Design, build, and operate scalable cloud-native data platforms and production-grade pipelines using Azure, Microsoft Fabric, and Databricks. Implement lakehouse/medallion architectures, transform data with SQL/PySpark/Python, enable BI and AI workloads, enforce data quality, governance and security, automate deployments with DataOps/IaC, optimize costs, produce runbooks/docs, and mentor junior data engineers.

Design and maintain enterprise semantic models and AI-ready data products, build and optimize Power BI datasets and DAX metrics, enforce governance and security, enable self-service and AI-assisted analytics, and collaborate with stakeholders and data engineers to support BI and ML integrations.

The Data Engineer will analyze and interpret data to drive insights, create dashboards, and enhance product performance, collaborating with cross-functional teams.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Design and operate distributed inference systems for LLMs, build large-scale data pipelines, and debug production issues while collaborating with researchers and ML engineers.

Build and maintain core financial backend systems (trading, ledger, billing), transaction processing, reconciliation, and portfolio state management. Design and scale real-time and batch data pipelines, architect APIs and backend services, ensure reliability and observability, and contribute to distributed system design (event-driven, microservices, streaming). Partner with ML teams as needed.

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 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.

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.

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.

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.

Senior Java Big Data Developer with 8+ years building applications on Cloudera/Hadoop ecosystems using Spark and MapReduce. Responsible for designing performant Java (1.8+) solutions, data analysis of large datasets, unit testing (JUnit), and collaborating with/managing global technology teams. Preferred skills include Unix/Python scripting and familiarity with Hive, Impala, YARN, and Kafka.

Design, build, and maintain scalable data systems and ETLs on GCP; connect production data to business systems; generate analytics and ML-driven insights for GTM teams; work cross-functionally and introduce modern tools (Airflow, DBT, Presto, Hightouch).

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.