Overview

Key Responsibilities • Design, develop, and maintain scalable data pipelines on AWS using services such as S3, Glue, Lambda, Redshift, and EMR.

Responsibilities

• Design, develop, and maintain scalable data pipelines on AWS using services such as S3, Glue, Lambda, Redshift, and EMR.
• Build and optimize data warehousing solutions using Snowflake, including performance tuning and data modeling.
• Write efficient and reusable code in Python and SQL for data transformation and processing.
• Collaborate with cross-functional teams, including data scientists, analysts, and business stakeholders, to understand data requirements.
• Develop and optimize solutions using graph databases (e.g., Neo4j, Amazon Neptune), including query design and performance tuning.
• Design, build, and operate vector database solutions (e.g., Milvus, Amazon OpenSearch) to support semantic search, recommendations, RAG, and AI-driven use cases.
• Integrate vector databases with LLM-based applications and AI workflows.
• Monitor, troubleshoot, and improve pipeline performance and reliability.
• Ensure data quality, integrity, and security across all stages of the pipeline.
• Participate in code reviews, architecture discussions, and continuous improvement initiatives.

Qualifications

• 5-8 years of experience in data engineering or related roles.
• Strong hands-on experience with AWS cloud services, including data and AI workloads.
• Deep understanding of Snowflake architecture, performance tuning, and best practices.
• Advanced proficiency in Python and SQL for data pipelines, transformations, and services.
• Strong understanding of graph and vector data modelling concepts and their practical applications.
• Hands-on experience with graph databases (e.g., Neo4j, Neptune) and vector databases (e.g., Milvus, Amazon OpenSearch).
• Excellent analytical and problem-solving skills.
• Strong communication and collaboration abilities.
• Bachelor's or master's degree in computer science, Engineering, or a related field.
• Familiarity with data governance and compliance practices.
• Exposure to real-time data processing frameworks (e.g., Kafka, Spark Streaming).
• Mode detail on Knowledge Base
• Hands-on expertise with vector databases, specifically Milvus, covering schema design, indexing, and optimizing write performance for large-scale embedding ingestion pipelines.
• Proficiency in building Knowledge Graph ingestion pipelines using Graph Databases — including entity extraction, relationship modelling, and populating nodes and attributes.
• Strong pipeline engineering skills in Python and frameworks for orchestrating multi-stage document processing workflows, with experience deploying and monitoring these pipelines in production environments.
• Bonus: Exposure to RAPIDS libraries (cuDF, cuML, cuGraph) or CUDA-based tooling for GPU accelerated data processing, enabling faster transformation and optimization during large-scale ingestion workflows.

Nice to have

• Knowledge of the NVIDIA ecosystem and its applications in data and AI.