Databricks Architect with AI

Role Overview:

We are looking for an experienced Databricks Data Architect to lead the design and implementation of enterprise-scale data platforms using Databricks Lakehouse Architecture. The candidate should have strong expertise in up-to-date data engineering, data architecture, dimensional modeling, and scalable cloud data solutions.

Key Responsibilities:

- Design and implement enterprise-scale data platforms using Databricks Lakehouse Architecture.
- Define end-to-end data architecture covering ingestion, transformation, storage, governance, and consumption layers.
- Establish architecture standards, coding guidelines, reusable design patterns, and best practices.
- Lead technical decisions related to scalability, performance, reliability, and security.
- Design and build scalable ETL/ELT pipelines using PySpark on Databricks.
- Architect Bronze, Silver, and Gold data transformation frameworks.
- Design and manage Delta Lake storage architecture, including optimization and data lifecycle management.
- Implement Databricks Workflows/Lakeflow pipelines for orchestration and automation.
- Define data quality, validation, reconciliation, and monitoring frameworks.
- Oversee CI/CD and DevOps practices for data engineering projects.
- Design enterprise data models using dimensional modeling techniques.
- Develop Star Schemas, Fact Tables, Dimension Tables, and conformed dimensions.
- Define business metrics, KPI layers, semantic models, and curated Gold datasets.
- Provide technical leadership and mentorship to data engineers and developers.
- Conduct architecture and code reviews and ensure adherence to standards.




- Troubleshoot complex technical issues and guide teams in solution design.
- Support solution proposals, estimation, technical governance, and architectural roadmap activities.
- Drive adoption of modern data engineering and cloud-native best practices.

Technical Skills:

- Mandatory: Data Architecture, Databricks Lakehouse Architecture, Databricks Workflows / Lakeflow, Delta Lake, Unity Catalog, Databricks SQL, PySpark and Spark SQL, ETL/ELT design, Dimensional Data Modeling, Star Schema, Fact & Dimension Modeling, Slowly Changing Dimensions (SCD), Metric View, Python, SQL, Git, CI/CD frameworks, Performance tuning, Data quality frameworks, Batch and incremental processing.
- Cloud & Integration: Azure Databricks (preferred), ADLS Gen2, Azure Data Factory (ADF), Event-based and batch ingestion, Data governance, metadata management, and security.
- Good to Have: Data warehouse modernization, legacy EDW migration, Data Mesh, Medallion Architecture, BI platforms (Power BI, Tableau, Looker).

Candidate Profile:

- 15 years of relevant experience in data architecture and enterprise data platforms.
- Strong hands-on experience with Databricks and modern data engineering practices.
- Proven experience designing scalable and high-performance data platforms.
- Strong expertise in data modeling, Lakehouse architecture, and cloud data solutions.
- Ability to define target-state architecture and technical roadmaps.
- Experience reviewing technical designs and code and guiding development teams.
- Strong communication skills with the ability to drive technical discussions with architects and business stakeholders.
- Ability to provide architectural governance across multiple workstreams.
- Strong leadership and mentoring capabilities.

📌 Data Architect - Databricks (India)
🏢 Hire Global
📍 India