Data Architect

Required Qualifications:
Master's in Computer Science or a related field.
7+ years of experience in data engineering
Expertise in Azure. Plus if expereinced in AWS as well.
Expertise in designing and architecting production-ready enterprise grade data pipelines.
Strong knowledge of Cloud services, NoSQL, Document DBs , including design, implementation, and optimization. (e.g. AWS open search or Google Cloud Platform Vertex AI, neo4j etc. Mongo, Dynamo, CosmosDB etc.)
Expertise in designing, loading, and querying NoSQL.
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, Google Cloud Platform, or Azure) and containerization technologies (Docker, Kubernetes).
Excellent communication skills and ability to explain complex technical concepts to both technical and non-technical stakeholders.
Preferred Qualifications:
Experience designing multi cloud hybrid solutions.
Experience in building/designing autonomous AI agents
Knowledge of natural language processing (NLP) technologies.
Experience working with Snowflake data warehouse, particularly in AI/ML contexts.
Familiarity with the telecom industry and its specific data challenges and use cases is a big plus.

Key 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.
Integrate data pipelines with Snowflake data warehouse for efficient data processing and storage.
Build POCs to compare evaluate various emerging technologies around data and AI
Create prototypes to establish patterns.