Description

Key Responsibilities
Architect end-to-end data platforms: ingestion, lakehouse (medallion), transformation, serving, and BI
Define reference architectures, standards, and best practices on Azure Databricks and Microsoft Fabric
Lead migrations from legacy DW/Hadoop/on-prem systems to the cloud lakehouse
Design for performance, cost optimization, security, and governance (Unity Catalog, Purview, RBAC)
Own solution proposals, estimations, and technical presales with client stakeholders
Guide data engineering teams and review design and code quality
Establish CI/CD, DevOps, and observability for data pipelines
Required Skills
15+ years in data engineering/architecture, with 5+ years on Azure data stack
Deep expertise in Azure Databricks (Spark, Delta Lake, Delta Live Tables, Unity Catalog, Workflows)
Strong hands-on with Microsoft Fabric (OneLake, Lakehouse, Data Factory, Warehouse, Power BI, Direct Lake)
Azure services: ADLS Gen2, Data Factory, Synapse, Key Vault, Event Hubs
Strong in Python/PySpark and advanced SQL
Data modeling, data governance, and security architecture
Experience with Terraform/Bicep, Azure DevOps/GitHub Actions
Good to Have
Databricks / Azure certifications (DP-203, DP-600, Databricks Architect)
MLOps / GenAI on Databricks exposure
Streaming architectures (Structured Streaming, Kafka)
Presales and client-facing consulting experience
Soft Skills
Strong stakeholder communication and executive presentation ability
Ability to mentor teams and drive technical decisions

Key Skills

Education

Bachelor's degree