Remote Data Architect

Summary

Join our team as a Big Data Solution Architect and lead the design and implementation of big data solutions, leveraging your expertise in Azure Data Tech Stack and analytical warehouses.

Requirements

• 10 to 20 years of Big Data Solution Achitecturing experience
• 5 to 10 years of hands-on Big Data Engineering experience
• Demonstrated successful implementation of Big Data Solutions
• Excellent communication and collaboration skills

Responsibilities

• Provide Big Data Solution Architecture advisory; this includes infrastructure, networking, cloud computing best practices, ingestion (streaming or batches) and implementation guidance
• Understand SP’s overall data estate, IT and business priorities and success measures to design implementation architectures and solutions
• Familiarity with the current Azure Data Tech Stack (e.g. SQL Server, Azure Synapse, Azure ML Studio, Azure Data Factory, Big Data, Data Lake, Azure Databricks, Fabric, Purview, IOT Hub etc.)
• Implemented solution with cost and compute monitoring thoroughly considered; Platform runs a charge back to customer model
• Ensure that solution exhibits high levels of performance, security, scalability, maintainability, appropriate reusability, and reliability upon deployment
• Maintain technical skills and knowledge, keeping up to date with market trends and competitive insights to inform improvement opportunities to the platform; not limited to Azure
• Implement solution with CI/CD and BAU long-term maintenance in mind
• Familiarity with analytical warehouses
• Maintains cost-efficiency and compute-efficiency mindset
• Point of Contact for SOW CRs that needs to be implemented into DataFabricks from time to time
• I) New Data Sources of different architecture types (pub-sub, DBs, APIs, etc.)
• Ii) Maintenance and refresh rates of data sources
• Fill in for the Principal Data Architect where necessary
• I) Includes overseeing the Senior Data Engineer and the Data Engineering Scrum Team
• Ii) Provide review and approval to Data Modelling pipelines that feed into Data Marts/Fabric DataFlows that are fed to Fabric Dashboards or ML use cases