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Specialized Snowflake Cortex and ELT skills limit applicants, but data engineering remains moderately competitive.
Snowflake and ELT skills are transferable, but Cortex AI and pharma domain preference increase specialization.
Multiple mandatory Snowflake, ELT, dbt, SQL and AI skills imply moderate shortlisting strictness.
Build and optimize end-to-end data ingestion and ELT pipelines in Snowflake, including use of advanced features like Streams, Tasks, and Dynamic Tables.
Leverage Snowflake Cortex for AI-powered data transformations such as text summarization, sentiment analysis, and semantic search, enabling AI-ready datasets for analytics and ML.
Ensure data models (star/snowflake schema) and data pipelines are performant, cost-efficient, and uphold data quality, security, and governance standards.
Strong hands-on experience with Snowflake platform and advanced SQL, including performance tuning and Snowflake features like Streams, Tasks, Dynamic Tables.
Experience with Snowflake Cortex functions and familiarity with generative AI concepts, NLP, and prompt engineering.
Proficiency in ELT/ETL tools (e.g., dbt), cloud platforms (AWS/Azure/GCP), and programming in SQL and Python.
Education: BE/B.Tech or Master of Computer Application; Work Experience Required: Not explicitly mentioned in the JD.
Experienced data engineer with demonstrated expertise in Snowflake-focused data lake/lakehouse architectures and AI-enabled data platform integration.
Skilled at translating complex AI and business use cases into scalable data pipelines and analytical models, with a track record of performance and cost optimization.
Strong in cross-functional collaboration with architects, analysts, and data scientists, comfortable working in Agile environments to deliver AI-enriched datasets.