Data Architect

Job Description 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, No
  
  SQL, Document DBs , including design, implementation, and optimization. (e.g. AWS open search or GCP Vertex AI, neo4j etc.
• Mongo, Dynamo, Cosmos
  
  DB etc.)
• Expertise in designing, loading, and querying No
  
  SQL.
• 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 Tensor Flow.
• 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.

• 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.

• 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.

• Snowflake, Databricks, Azure, DB Architecture.