As a Strong Databricks / AWS Data Architect, your role will involve the following key responsibilities:
Must have minimum 5+ years of experience in Data Architecture / Data Engineering, with exposure in enterprise-scale data platform modernization initiatives
Must have minimum 3+ years of deep hands-on experience in Databricks-based lakehouse architecture on AWS, including large-scale data platform implementations
Strong expertise in Databricks ecosystem including Delta Lake, Databricks SQL, Unity Catalog, Delta Live Tables, and MLflow with focus on performance optimization and security
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
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
Must have hands-on experience building scalable ingestion frameworks including batch, streaming, and CDC pipelines using tools like Kafka, Kinesis, Spark, or similar technologies
Proven experience implementing CI/CD pipelines for data platforms, including infrastructure as code, automated deployments, and environment management
Hands-on experience enabling data platforms for AI/ML and Generative AI use cases, including feature stores, vector storage, and secure data access patterns
Experience with orchestration tools such as Apache Airflow or MWAA and designing integration layers for analytics, BI, and AI consumption
Additionally, it is preferred if you have experience working with Product Companies and hold certifications in AWS, Databricks, or Snowflake. Exposure to MDM, data quality frameworks, and enterprise metadata tools would be an added advantage. Please click on Apply to know more about this opportunity. As a Strong Databricks / AWS Data Architect, your role will involve the following key responsibilities:
Must have minimum 5+ years of experience in Data Architecture / Data Engineering, with exposure in enterprise-scale data platform modernization initiatives
Must have minimum 3+ years of deep hands-on experience in Databricks-based lakehouse architecture on AWS, including large-scale data platform implementations
Strong expertise in Databricks ecosystem including Delta Lake, Databricks SQL, Unity Catalog, Delta Live Tables, and MLflow with focus on performance optimization and security
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
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
Must have hands-on experience building scalable ingestion frameworks including batch, streaming, and CDC pipelines using tools like Kafka, Kinesis, Spark, or similar technologies
Proven experience implementing CI/CD pipelines for data platforms, including infrastructure as code, automated deployments, and environment management
Hands-on experience enabling data platforms for AI/ML and Generative AI use cases, including feature stores, vector storage, and secure data access patterns
Experience with orchestration tools such as Apache Airflow or MWAA and designing integration layers for analytics, BI, and AI consumption
Additionally, it is preferred if you have experience working with Product Companies and hold certifications in AWS, Databricks, or Snowflake. Exposure to MDM, data quality frameworks, and enterprise metadata tools would be an added advantage. Please click on Apply to know more about this opportunity.