Data Architect

Project Role : Data Architect
Project Role Description : Define the data requirements and structure for the application. Model and design the application data structure, storage and integration.
Must have skills : Data Architecture Principles
Good to have skills : NA
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary: We are looking for a highly experienced and technically proficient Generative AI Data Architect (AWS) with deep expertise in data architecture principles, data modeling techniques and methodologies, and enterprise-scale AI/ML systems. The ideal candidate should have 10-13 years of experience in designing and managing complex data platforms, with a strong foundation as a Data Modeler. This role will lead architectural decisions for Gen AI and LLM-based systems, ensuring the data layer is optimized for performance, scalability, and AI integration. Roles & Responsibilities: 1. Architect scalable and modular data platforms on AWS to support Gen AI, LLMs, and advanced analytics use cases. 2. Lead and own data modeling efforts across conceptual, logical, and physical layers, ensuring models align with AI/ML application needs. 3. Define, implement, and govern data architecture principles, data flow patterns, lineage, and transformation logic. 4. Integrate and manage LLMs, vector databases, and semantic search infrastructure (e.g., RAG pipelines). 5. Translate Gen AI requirements into data architecture designs, leveraging cloud-native patterns and tools. 6. Collaborate with data engineers, ML scientists, and application developers to ensure data infrastructure meets the performance, scalability, and security needs of Gen AI applications. 7. Establish data governance, quality, and metadata management frameworks. 8.Drive data lifecycle management, lineage tracking, and model optimization for both structured and unstructured data assets. 9.Conduct design reviews, build architecture documentation, and advise on Gen AI tool selection and integration strategies. 10.Lead the design and deployment of real-time and batch data pipelines that power Gen AI applications. Professional & Technical Skills: 1. Data Modeling Expertise. 2. Strong command of data modeling techniques including 3NF, dimensional modeling (star/snowflake schemas), data vault, and ontology modeling. 3. Experience using tools like Erwin, ER/Studio, dbt, or SQL Power Architect. 4. Ability to model and optimize data for AI/LLM-ready formats (e.g., text embeddings, vector representations). 5. In-depth knowledge of data lake, Lakehouse, data warehouse, and data mesh architectures. 6. Experience designing end-to-end data platforms for AI/ML workloads using AWS native tools. 7. Gen AI & LLMs:Practical experience integrating LLMs (e.g., OpenAI, Cohere, Hugging Face, Bedrock) into applications. 8. Proficiency in vector databases (e.g., FAISS, Pinecone, Weaviate) and building RAG (Retrieval-Augmented Generation) architectures. 9. Familiar with prompt engineering, embedding models, and LLM orchestration frameworks (e.g., Lang Chain). 10. Cloud & Infrastructure: Advanced experience with AWS services: S3, Redshift, Glue, Lake Formation, Athena, Lambda, SageMaker, Bedrock. 11. Proficient in Terraform or CloudFormation for infrastructure provisioning. 12. Data Engineering: Experience with ETL/ELT pipelines, streaming (Kafka, Kinesis), batch processing (Spark, Glue). 13. Strong hands-on skills in SQL, Python, PySpark, and API integrations. 14. Governance & Security: Deep understanding of data governance, lineage, data cataloging (e.g., AWS Glue Catalog, Amundsen), and compliance (GDPR, HIPAA). 15. Familiarity with role-based access, IAM, encryption (KMS), and secure data architectures. 16. Architecture Frameworks: Knowledge of TOGAF, Zachman, or enterprise architecture best practices. Additional Information: Experience: 10 to 13 years of experience in data architecture, data modeling, and enterprise-scale AI/ML projects Education: Bachelor's or master's degree in computer science, Data Engineering, Information Systems, or related fields.