Role & Responsibilities :

- Lead enterprise-scale implementation of data warehouse data platforms on Databricks and Snowflake environments.

- Design and implement Medallion (Bronze/Silver/Gold) architecture and scalable enterprise data models.

- Establish data modeling standards (dimensional, data vault, lakehouse patterns) and ensure best practices across projects

- Establish enterprise data governance frameworks including cataloging, lineage, stewardship, and compliance using Atlan.

- Define and implement CI/CD pipelines for infrastructure and data platform deployments

- Design data architectures that support AI/ML and Generative AI workloads including vector storage, feature layers, and secure access patterns.

- Build scalable ingestion frameworks supporting batch, streaming, and CDC pipelines.

- Architect secure, high-performance data integration layers for analytics, BI, and AI consumption.

- Develop target-state architecture blueprints and enforce data standards, governance, and best practices across teams.

- Collaborate with engineering, analytics, and data science teams to ensure platform alignment and scalability.

- Engage with clients as a trusted advisor, driving data strategy, roadmap definition, and identifying opportunities for expansion.

Ideal Candidate :

- Strong Databricks / AWS Data Architect profile

- Mandatory (Experience 1) : - Must have minimum 8+ years of experience in Data Architecture / Data Engineering, with exposure in enterprise-scale data platform modernization initiatives

- Mandatory (Experience 2) : - Must have minimum 3+ years of deep hands-on experience in Databricks-based lakehouse architecture on AWS, including large-scale data platform implementations

- Mandatory (Experience 3) : - Strong expertise in Databricks ecosystem including Delta Lake, Databricks SQL, Unity Catalog, Delta Live Tables, and MLflow with focus on performance optimization and security

- Mandatory (Experience 4) : - Strong experience with AWS data services including S3, Glue, EMR, Lambda, Redshift, Athena, Lake Formation, and DMS, with strong understanding of cloud-native architecture patterns

- Mandatory (Experience 5) : - Proven experience designing and implementing Medallion (Bronze/Silver/Gold) architecture, scalable data models (Dimensional/Data Vault), and enterprise lakehouse platforms supporting batch and real-time processing

- Mandatory (Experience 6) : - Must have hands-on experience building scalable ingestion frameworks including batch, streaming, and CDC pipelines using tools like Kafka, Kinesis, Spark, or similar technologies

- Mandatory (Skill 1) : - Proven experience implementing CI/CD pipelines for data platforms, including infrastructure as code, automated deployments, and environment management

- Mandatory (Skill 2) : - Hands-on experience enabling data platforms for AI/ML and Generative AI use cases, including feature stores, vector storage, and secure data access patterns

- Mandatory (Skill 3) : - Experience with orchestration tools such as Apache Airflow or MWAA and designing integration layers for analytics, BI, and AI consumption

Preferred (Company) : - Product Companies.

- Preferred (Certification) : - AWS / Databricks / Snowflake certifications; experience with Snowflake alongside Databricks; exposure to MDM, data quality frameworks, and enterprise metadata tools