Senior Data Engineer

Job Description:

• 5-7 years of overall experience in Data Engineering, building scalable and production-grade data platforms.
• 3+ years of hands-on experience with AWS services, including IAM, API Gateway, EC2, S3, and related cloud services.
• 2+ years of experience in containerization and orchestration, including creating, deploying, and managing containers using Docker and Kubernetes.
• 2+ years of experience building and managing CI/CD pipelines using tools such as Jenkins, GitHub Actions, or equivalent.
• 2+ years of hands-on experience with Snowflake, including data warehousing, data modelling, performance optimization, and integration with ETL/ELT pipelines.
• 5-7 years of strong experience with ETL/ELT frameworks and data integration, including designing reliable and scalable data pipelines.
• 5-7 years of experience with Big Data technologies such as Apache Spark and Kafka, including large-scale data processing and streaming workloads.
• Strong programming experience in Python, with working knowledge of Java or Scala for data engineering applications.
• AI / Generative AI experience: Exposure to integrating LLMs into enterprise applications and data platforms, including prompt engineering, embeddings, RAG (Retrieval-Augmented Generation), vector databases, and LLM APIs.
• Agentic AI: Understanding or hands-on experience building AI agents and agentic workflows, including tool/function calling, multi-step reasoning workflows, orchestration, and integration of agents with enterprise data sources and APIs.
• AI Data Engineering: Experience preparing and managing structured/unstructured datasets for AI/ML and GenAI workloads, including data ingestion, transformation, chunking, metadata management, embeddings, and data quality/governance.

Good to have:

• Exposure to frameworks and platforms such as LangChain, LangGraph, LlamaIndex, Amazon Bedrock, or Azure OpenAI for developing enterprise GenAI or Agentic AI solutions.

Skills

AWS, Data Engineering, Snowflake