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
Skills: aws,snowflake,pipelines,access,architecture,data,enterprise
