Solution Architect - Azure Databricks

Job Description : About Quantzig :

Quantzig is a global analytics and advisory firm with offices in the US, UK, Canada, China, and India. We have assisted our clients across the globe with end-to-end advanced analytics, visual storyboarding, Machine Learning and data engineering solutions implementation for prudent decision making. We are a rapidly growing organization that is built and operated by high-performance champions.

If you have what it takes to be the champion with business and functional skills to take ownership of an entire project end-to-end, help build a team with great work ethic and a drive to learn, you are the one we are looking for. The clients love us for our solutioning capability, our enthusiasm and we expect you to be a part of our growth story.

Solution Architect Azure Databricks Data & ML Platform Role Overview :

We are seeking an experienced Solution Architect with strong expertise in Azure Databricks, enterprise data platforms, edge-to-cloud integration, and MLOps to lead architecture design and implementation across large-scale industrial and analytics ecosystems. The role involves designing scalable data platforms, enabling production-grade machine learning environments, and integrating OT systems with enterprise cloud data infrastructure. This role is ideal for architects working in client-facing consulting environments, delivering secure, governed, and scalable solutions across multi-layer data platforms and edge-connected intelligent systems.

Key Responsibilities : 1. Architect Integrated Data & ML Solutions :

• Lead architecture design across edge, cloud, and ML platform layers.

• Define scalable architectures supporting telemetry ingestion, operational analytics, and ML-driven insights.

• Design zone-based lakehouse architectures aligned with enterprise governance standards.

2. Build and Govern Enterprise Data Platform :

• Architect ingestion and transformation pipelines across Raw - Bronze - Silver - Gold layers.

• Implement enterprise-grade Unity Catalog-based governance frameworks.

• Define consumption-layer strategies supporting BI, ML, and operational workloads.

3. Enable Scalable Machine Learning Platform :

• Architect infrastructure supporting feature engineering, training, validation, versioning, and deployment.

• Enable Feature Store and Model Store implementations using Azure Databricks ML workspace.

• Support cloud and edge inference deployment strategies.

• Define lifecycle cadence models : 1. Weekly refresh, 2. Monthly retraining, 3. Quarterly model revamp.

4. Edge Integration and Automation :