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Data Scientist on a 10-person Alpha Science team at a large Canadian pension fund, building data pipelines, ML models, and dashboards in Python and SQL to support investment decision-making across asset classes using market and alternative datasets.
Designs and optimizes enterprise data platforms for analytics and AI, focusing on scalable, secure data pipelines, lakehouses, and warehouses using Microsoft Fabric and Azure.
Leads AI/data engineering solutions on RBC’s hybrid multi-cloud platform, designing scalable pipelines, RAG systems, and agentic workflows to meet business objectives. Mentors teams and ensures robust, governed data products for financial AI applications.
Machine Learning Engineer at Manulife designing, building, and operating ML/AI platforms and pipelines from experimentation to production at scale, using Python, CI/CD tools, Docker/Kubernetes, Terraform, and Azure cloud services.
This role blends 40% data architecture with 60% hands-on Power BI development for a public sector or healthcare client. Core technologies include Power BI, Databricks, Azure Data Factory, Synapse, and SQL Server.
Build scalable data pipelines connecting industrial plants to Azure and Databricks, enabling ML models and analytics for production, energy, and logistics efficiency.
Designs and maintains an Azure data platform for real-time IoT telemetry ingestion and analytics, enabling sales, support, and operations with scalable pipelines and AI-ready infrastructure.
Senior Azure Data Platform Engineer building and managing a lakehouse-architecture data platform, driving the transition to Microsoft Fabric while ensuring data quality, scalability, and performance.
Data Platform Engineer responsible for designing, building, and maintaining a modern Azure-based lakehouse platform (Delta Lake, medallion architecture) with ETL/ELT pipelines, while driving the transition to Microsoft Fabric.
Data Engineer designing and optimizing data pipelines in Azure Databricks environments using Python, SQL, and IaC/CI-CD for managed-services clients.
Designs, builds, and optimizes Azure Databricks-based data pipelines and lakehouses for clients, ensuring reliability, performance, and security while advising on AI and governance improvements.
Design and build scalable Microsoft-based data pipelines and Power BI reports to enable data-driven decisions in a biotech company.
Data Engineer owning an Azure data platform for a climate tech hardware company, building real-time and batch pipelines with Event Hubs and Databricks to connect IoT telemetry data to business teams.
Senior Data Engineer building scalable data platforms and pipelines using Python, PySpark, and SQL on a lakehouse architecture within VodafoneZiggo's Data & AI team, in a hybrid work setup.
The Data & AI Platform Engineer will design and develop modern data platforms, pipelines, and analytics architectures for the Swiss healthcare sector. The role involves managing data governance, integrating diverse data sources, and collaborating with AI and application teams to build robust data-driven solutions.
Data & AI Product Manager responsible for shaping strategy, roadmap, and requirements for data and AI products, partnering with an agile engineering pod using SQL and modern data platform concepts like lakehouse architecture at an employee-owned engineering and professional services firm.
Build and maintain Samsara’s petabyte-scale data lake and distributed compute platform, designing ingestion pipelines, lakehouse infrastructure, and internal tooling to power analytics and AI across the company.
The Principal AI Engineer will lead the design and implementation of agentic AI systems and data platforms within a financial services environment. The role involves building RAG solutions, scalable data pipelines, and AI observability frameworks using technologies like Python, Spark, and Databricks.
Design and build enterprise-scale data platforms and pipelines for NZTA’s mobility data, enabling analytics, AI, and better transport outcomes across New Zealand.
Designs and maintains scalable data platforms on Azure and Databricks, focusing on ETL/ELT pipelines, data lakehouse architecture, and cloud-native migrations while ensuring data quality, governance, and DevOps automation.
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