Skills Required:

• Data Platform - Data Lake, Data Warehouse, Data Management, BI Analytics & Data Science Platform.
• Data Engineering Workloads - Sourcing, Ingesting, Distributed Layered Storage, History, Warehousing, Data Mart.
• Data Sourcing Approach - Batch, Real-time Streams, Change Data Capture, Bulk REST API
• Data Modelling design covering Landing-Base-Quality-Transformed, Dimension (SCD), Facts, Star Schema.
• Data Storage approach covering Interim, Base, History, Archive, Formats, Schema registry, Schema Evolution.
• Designing Interactive, intuitive & fast User Consumption & Semantic layers using Data Hub & BI concepts.
• In-Depth understanding of Low level OS/DB/Server Security plus Data Security - at rest, In Motion & at use.
• System tool Integration experience with DataLake, DWH, DM, BI, DS Platform following enterprise guidelines.
• In-Depth Internal working know-how of Clustered Distributed Architecture covering Resource Management, Data Partitioning, Fault Tolerance etc.
• Data Platform Management concepts - Scalability, HA, Failover, DR, monitoring, Tuning, Workload, performance, troubleshooting, release.
• Implementation experience overseeing setup of Platform Tools covering Security Hardening, Multi-tenant, HA, DR Cluster, HA.
• Deployment Architecture implementation experience in Hybrid system with mix of On-premise (VM/PM), Private - Public Cloud.
• Designing Data Operations workloads covering Orchestration, Containers (Kubernetes, Docker & Open Shift) & CI/CD Pipeline
• Technical Roadmap detailing for Data Platform covering Obsolescence, Upgrades (Major / Minor), Re-Architecture, Re-Platforming