Azure Data Platform Technical Lead - AI / Data Engineering
SoftPathTechnologies
Job Title: Azure Data Platform Technical Lead AI / Data Engineering
Position: FTT
Location: Redmond, WA
Job Summary
We are seeking an experienced Azure Data Platform Engineer / Technical Lead to build and support enterprise-scale data platforms and backend services. The successful candidate will have strong hands-on experience across Azure Data Engineering, cloud services, data pipelines, APIs, microservices, and analytics platforms .
Experience with AI/ML or AI-enabled analytics is highly desirable.
Core Technical RequirementsAzure Data Engineering
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Azure Databricks
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Apache Spark / PySpark
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Azure Synapse Analytics
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Azure Data Factory
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ADLS Gen2
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Delta Lake
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ETL / ELT
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Data Warehousing
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Data Modeling
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Lakehouse / Medallion Architecture
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Bronze, Silver, and Gold data layers
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Data Quality and Reconciliation
Databases & Analytics
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SQL Server / Azure SQL
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Cosmos DB
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Kusto / Azure Data Explorer
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Experience handling large-scale enterprise datasets
Backend Development
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C#
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.NET / ASP.NET Core
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REST APIs
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Microservices
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Azure Functions
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Event Hubs
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Service Bus
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Entity Framework Core
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Authentication and Authorization
Cloud & DevOps
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Microsoft Azure
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Azure DevOps
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Git
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CI/CD
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Azure Key Vault
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Azure Monitor
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Application Insights
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Docker
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Kubernetes / AKS
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Design and implement enterprise Azure data platforms
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Build scalable data ingestion and transformation pipelines
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Develop Lakehouse-based analytics solutions
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Build APIs and backend services for data integration
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Implement event-driven and microservices architectures
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Modernize existing data and application platforms
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Support cloud migrations and data modernization initiatives
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Implement monitoring, logging, telemetry, and alerting
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Develop CI/CD pipelines and support production deployments
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Ensure secure access to enterprise data and cloud resources
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AI/ML or AI-enabled analytics
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React / Angular
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TypeScript
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GraphQL
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Power BI
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Infrastructure as Code
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SRE / production support experience
The ideal candidate is a hands-on technical lead who can work across the complete data platform lifecycle:
Data Sources Data Ingestion Processing Lakehouse Analytics APIs Cloud Services Monitoring Production
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