Data Architect – Databricks / Fabric Data Architect Lead

Data Architect – Databricks / Fabric Data Architect Lead

 

Section I - Job Details

Job ID:

Coding: Non-mandatory

Experience Level:

• Min: 10 yrs
• Max: 15 yrs

Location: Ahemdabad / /

Work Type: Onsite  / /

Section II - Job Evaluation Topics and Weight

• Topic of Evaluation / Skills: Lakehouse Architecture (bronze/silver/gold, semantic models)
  Mandatory: Yes
  Percentage: 30%
• Topic of Evaluation / Skills: Governance & Security (Unity Catalog, lineage/privacy)
  Mandatory: Yes
  Percentage: 20%
• Topic of Evaluation / Skills: Performance Optimization (partitioning, Z-Order, cluster tuning)
  Mandatory: Yes
  Percentage: 20%
• Topic of Evaluation / Skills: Data Ingestion & Pipelines (deploy/test/upgrade)
  Mandatory: Yes
  Percentage: 20%
• Topic of Evaluation / Skills: Domain Alignment & Long-term Data Strategy
  Mandatory: No
  Percentage: 10%

Section III - Job Requirements and Responsibilities

Job Requirements:

• 10–15 years overall experience with 5–6 years architecting Databricks/Fabric platforms
• Experience owning Databricks/Fabric Lakehouse architecture end-to-end
• Ability to design ingestion, modeling layers, and semantic models

Key Responsibilities:

• Design & own Lakehouse architecture (bronze/silver/gold) and semantic models
• Implement governance using Unity Catalog, lineage, and privacy controls
• Optimize pipelines through partitioning, Z-Order, and cluster tuning
• Provide a SQL-centric data exploration experience on Databricks
• Create, deploy, test, and upgrade complex data pipelines
• Align architecture to business domains such as aerospace CES; drive long-term data strategy
• Participate in planning and execution of complex cross-team projects

Section IV - Job Qualifications & Skills

Domain: Good to have Aerospace (Aviation)/Manufacturing Domain knowledge

Soft Skills:

• Architectural leadership
• Stakeholder management
• Documentation & standards

Education Requirements: Bachelor's/Master's in Computer Science or equivalent (optional/flexible)

Section V - Sample Questions for Training Model

Technical:

1. Design a Databricks Lakehouse for multi-domain analytics. How would you structure bronze/silver/gold layers?
2. What governance model would you implement with Unity Catalog for cross-domain data access?
3. How do you approach performance optimization (partitioning, Z-Order, cluster sizing)?

Coding:

1. Write SQL to create a Delta Live Tables pipeline skeleton with quality expectations.
2. Provide a SQL example that demonstrates partition pruning and Z-ORDER impact.