Data Platform Architec

Key Responsibilities Design enterprise data platforms using AWS and Databricks Lakehouse Define platform architecture for data ingestion, transformation, governance, analytics, and AI workloads Own Databricks workspace design, setup, access control, cluster policies, Unity Catalog, and environment strategy Build reusable platform patterns for dev, test, validation, and production environments Lead infrastructure automation using Terraform, GitHub Actions, CI/CD, and DevOps best practices Design and govern data pipelines using Airflow, dbt, PySpark, SQL, and Databricks Workflows Enable data cataloging, lineage, and governance using tools like Atlan, Unity Catalog, and Lake Formation Architect GenAI solutions using AWS Bedrock, Bedrock Knowledge Bases, vector stores, embeddings, OpenSearch, RAG pipelines, and guardrails Support AI use cases such as document intelligence, semantic search, clinical/regulatory copilots, summarization, and knowledge assistants Design secure analytics and data science environments using Posit, EKS, Databricks, and AWS services Ensure platform design follows pharma compliance expectations including GxP, auditability, data integrity, RBAC, and controlled release processesRequired Skills Strong experience with AWS, Databricks, Delta Lake, Unity Catalog, S3, IAM, KMS, Glue, Lambda, Step Functions, EKS, and OpenSearch Hands-on knowledge of Terraform, GitHub, CI/CD, Airflow, dbt, Python, PySpark, and SQL Experience with AI/GenAI architecture, including knowledge bases, vector databases, embeddings, RAG, Bedrock, and prompt / model governance Understanding of pharma data domains and regulated environments Strong architecture, stakeholder management, and technical leadership skillsPreferred Background 10+ years of experience in data engineering, cloud architecture, or platform engineering Experience in pharma, life sciences, healthcare, or regulated industries