Job Summary :

We are looking for an experienced Databricks Data Architect to design and lead the implementation of scalable, secure, and high-performance data platforms. The ideal candidate should have strong expertise in Databricks, Lakehouse architecture, cloud platforms, and enterprise data strategy. The role will drive data architecture decisions, support AI/ML initiatives, and align technology solutions with business objectives.

Must Have Skills :

- Databricks

- Lakehouse Architecture

- Delta Lake

- Lakeflow

- Lakebase

- Databricks Apps

- Declarative Automation Bundle (DAB)

- CI/CD

- ETL

- Data Ingestion

- Data Integration

- Data Modeling

- Data Warehouse

- GCP

- FinOps

- AI

- ML

- MLFlow

Key Responsibilities :

Data Architecture & Strategy :

- Define and implement enterprise data architecture roadmaps.

- Design modern data platforms using Lakehouse architecture on Databricks.

- Establish data modeling standards, including dimensional and medallion architecture.

- Design data ingestion, transformation, storage, and consumption layers.

- Define Databricks workspace architecture, cluster sizing, workload optimization, and governance models.

- Architect job orchestration and scheduling frameworks.

- Enable integration with secret management systems, external APIs, enterprise applications, and AI/LLM services.

- Perform capacity planning, sizing, and cost estimation for Databricks workloads.

Databricks Platform Leadership :

- Design and optimize solutions using Databricks, Delta Lake, Unity Catalog, Lakeflow, Lakebase, Databricks Apps, and Workflows.

- Develop ETL/ELT pipelines using PySpark, SQL, and notebooks.

- Implement Delta Lake capabilities such as ACID transactions, time travel, and schema evolution.

- Drive Databricks best practices, performance tuning, and cost optimization.

- Design cloud landing zone architecture and governance frameworks.

- Ensure secure data access and network isolation for enterprise platforms.

Cloud Data Engineering :

- Design scalable solutions on GCP, AWS, and Azure.

- Integrate Databricks with cloud data services and enterprise systems.

- Build batch and real-time data processing solutions.

Data Governance & Security :

- Implement data governance frameworks and metadata management.

- Ensure data quality, lineage, cataloging, and compliance.

- Define and enforce security, access management, and governance standards.

Stakeholder Management :

- Work closely with business stakeholders, data scientists, and engineering teams.

- Convert business requirements into scalable technical solutions.

- Provide architecture leadership across multiple projects.

Performance & Optimization :

- Optimize data pipelines, storage, and compute utilization.

- Implement monitoring, logging, and performance tuning practices.

- Ensure platform scalability, reliability, and availability.

Team Leadership :

- Mentor data engineers and architects.

- Establish coding standards, reusable frameworks, and best practices.

- Lead design reviews and architecture governance discussions.