Snowflake Data Engineer

Job Title: Snowflake Data Engineer

Location: Austin TX 

Duration: Contract 

In person interview so need local only 

Must have:

• Hands-on experience with writing Complex queries using – Joins, Self Joins, Views, Materialized Views, Cursor also Recursive, use of GROUP BY, PARTITION BY functions / SQL Performance tuning
• Hands-on experience with ETL and Dimensional Data Modelling – Slowly Changing Dimensions (SCD – Type 1, 2, 3)
• Good understanding of concepts like schema types, table types - fact-dimension etc. like how to design a dimension vs fact, design considerations factored etc.
• Proficiency in Python scripting/programming – using Pandas, PyParsing, Airflow.
• Pandas, Tableau server modules, Numpy, Datetime, Apache Airflow related modules, APIs
• Setting up Python scripts on DataLab, scheduling processes, connecting with DataLake (S3 etc )
• Data Pipeline automation
• Strong Python programming skills
• Apache Kafka and Python (using client libraries like Confluent's librdkafka or kafka-python; to send (produce) and receive (consume) messages from Kafka topics.
• Experience building streaming applications, data pipelines, and microservices etc.)
• Good understanding on Snowflake Architecture - experience with designing and building solutions.
• Architecture, design aspects, performance tuning, time travel, warehouse concepts - scaling, clustering, micro-partitioning
• Experience with SnowSQL, Snowpipe

Good to Have 

• Experience with Snowflake performance optimization techniques
• Experience with Vertica, Singlestore
• Lead Experience - Experience interacting with business and independently develop and lead data projects. Collaborating with Offshore and owning overall project delivery.
• Actively participating in discussions with business to understand requirements, perform thorough impact analysis and provide suitable solutions.
• Experience with AI (very beneficial) and advanced AI integration –
• Good experience with Gen AI and LLM Integration which includes:
  • Having good understanding of RAG
  • Prompt and Context Engineering – structure, query and manage data context fed to LLMs
  • Vector Data Management – handling and storing data (also unstructured) in vector databases, indices for semantic search and RAG
  • Experience with LLM Orchestration frameworks – LangChain, LlamaIndex