- Mandatory (Experience 1) - Must have a minimum of 5+ years of experience in Data Architecture / Data Engineering, with exposure in enterprise-scale data platform modernization initiatives

- Mandatory (Experience 2) - Must have a minimum of 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 :

- 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.

Job-Specific Criteria :

- CV Attachment is mandatory

- Reason for change from last 2 organization (In detail)

- Provide CTC Breakup (Fixed + Variable)

What is your official notice period?

- Are you serving notice or available to join within 15 days?

- Have you had any career gaps or frequent job switches? If yes, please explain in detail.

- What's your preferred work location (Bengaluru / Hyderabad / Mumbai / Gurugram)

Interview Process :

- Technical Round 1

- Technical Round 2

- HR Round

Role & Responsibilities :

- Strong Databricks / AWS Data Architect profile

- Mandatory (Experience 1) - Must have a minimum of 5+ years of experience in Data Architecture / Data Engineering, with exposure in enterprise-scale data platform modernization initiatives

- Mandatory (Experience 2) - Must have a minimum of 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