Role & Responsibilities
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 (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 (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
Skills: data,architecture,airflow,snowflake,aws,access,enterprise,platforms,lake
