- Mandatory (Experience 1) : Must have minimum 5+ 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

Ideal Candidate :

- Strong Databricks / AWS Data Architect profile

- Mandatory (Experience 1) : Must have minimum 5+ 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 (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