Key Responsibilities

- Design, develop, and maintain scalable, secure, high-performance data pipelines using AWS services such as S3, Glue, Lambda, Redshift, EMR, and Step Functions
- Develop and optimize enterprise-scale data warehousing solutions using Snowflake, including data modeling, performance tuning, and cost optimization
- Build effective ETL/ELT pipelines using Python and SQL for large-scale data ingestion, transformation, and processing
- Design and implement data integration solutions across structured and unstructured data sources
- Develop and maintain graph and vector database solutions to support AI, machine learning, and LLM-based applications
- Collaborate with data scientists, business analysts, product teams, and stakeholders to translate business requirements into scalable data solutions
- Monitor, troubleshoot, and optimize pipeline performance, reliability, and data quality
- Implement best practices for data governance, security, compliance, and lifecycle management
- Participate in architecture discussions, technical design reviews, and code reviews
- Maintain CI/CD pipelines and version-controlled repositories using Git and Agile practices
- Use Jira or Azure DevOps (AzDO) boards for sprint planning, backlog management, and Agile project tracking

Required Skills

- Cloud & Data Engineering: AWS (S3, Glue, Lambda,



Redshift, EMR, Step Functions)
- Data Warehousing: Snowflake, Data Modeling, Performance Tuning, Query Optimization, Data Architecture
- Programming: Python, SQL (Advanced)
- Databases: Graph Databases (Neo4j / Amazon Neptune), Vector Databases (Milvus / Amazon OpenSearch Vector Engine)
- Data Engineering Concepts: ETL/ELT Development, Data Pipeline Design, Data Transformation, Data Integration, Data Quality Management
- DevOps & Agile: Git, Git Workflows, Azure DevOps (AzDO), Agile/Scrum Methodology

Preferred Qualifications

- Experience with workflow orchestration tools (AWS Step Functions)
- Knowledge of data governance, metadata management, and regulatory compliance
- Experience with real-time data processing technologies (Apache Kafka, Spark Streaming)
- Exposure to containerization and cloud-native deployment (Docker, Kubernetes)
- Experience designing AI-ready data platforms for ML/GenAI workloads

Nice to Have

- Working knowledge of the NVIDIA AI ecosystem and GPU-accelerated computing
- Experience with RAPIDS libraries (cuDF, cuML, cuGraph)
- Familiarity with CUDA-based technologies for large-scale data transformation
- Exposure to LLM frameworks, Retrieval-Augmented Generation (RAG), and AI data pipelines

📌 Data Engineer (Hyderabad)
🏢 Nxtwave Disruptive Technologies (Hiring for a client)
📍 Hyderabad