Data Architect

10+ years of experience in data engineering, with at least 3 years focused on Generative AI technologies. Expertise in designing and architecting production-ready enterprise grade data pipelines. Strong knowledge of vector databases, graph databases, NoSQL, Document DBs , including design, implementation, and optimization. (e.g. AWS open search or GCP Vertex AI, neo4j etc. Mongo, Dynamo, CosmosDB etc.) Expertise in designing, loading, and querying NoSQL, graph databases. Extensive experience in processing and leveraging unstructured data for data applications. Strong programming skills in Python and knowledge of deep learning frameworks such as PyTorch or TensorFlow. Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes). Excellent communication skills and ability to explain complex technical concepts to both technical and non-technical stakeholders. Job Responsibilities Hands on experience designing and developing end-to-end data pipelines, from data ingestion to pipeline deployment and monitoring. Lead the architecture and implementation of complex agentic frameworks, ensuring best practices in software engineering and data integrations. Collaborate with cross-functional teams (Technology, Business, Platform etc.) to integrate data solutions into existing data products and services. Stay at the forefront of GenAI advancements and incorporate new technologies and methodologies into our systems. Mentor and guide junior data engineers and architects in best practices for development. Design and implement optimized RAG architectures and pipelines. Design and implement strategies for handling unstructured data in pipelines. Design and implement graph database solutions for complex data relationships. Integrate data pipelines with Snowflake data warehouse for efficient data processing and storage. Build POCs to compare and evaluate various emerging technologies around data and AI.

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