Project LEad
Role Overview
Snowflake Developer role for a Snowflake-based data lakehouse program using Snowflake Cortex.
Builds and optimizes data pipelines and analytical datasets, including AI-driven transformations and intelligent data applications.
The role supports analytical work in the pharma and life sciences domain.
Key Skills for This Role
Full Job Posting
Role Overview
Snowflake Developer role for a Snowflake-based data lakehouse program using Snowflake Cortex.
Builds and optimizes data pipelines and analytical datasets, including AI-driven transformations and intelligent data applications.
The role supports analytical work in the pharma and life sciences domain.
Data Engineering and AI Responsibilities
- Develop scalable batch and near-real-time data ingestion pipelines.
- Load database, API, file, and streaming data into Snowflake, including JSON, Parquet, Avro, and CSV.
- Integrate Snowflake with S3, Azure Data Lake, and GCS.
- Build ELT pipelines with Snowflake SQL, Streams, Tasks, Dynamic Tables, and dbt across Bronze, Silver, and Gold layers.
- Use Snowflake Cortex for summarization, sentiment analysis, classification, and natural-language enrichment.
- Work with vector embeddings, similarity search, AI-ready datasets, and AI/ML teams.
- Design star and snowflake schemas, fact tables, and dimension tables for BI and AI.
- Optimize SQL, workloads, compute use, and Cortex consumption.
- Implement data validation, governance, RBAC, masking, row-level security, documentation, and lineage.
- Work in Agile processes with architects, analysts, and AI/ML teams.
Required Skills and Qualifications
- Strong hands-on Snowflake experience and advanced SQL performance tuning skills.
- Experience with Snowflake Streams, Tasks, Dynamic Tables, Time Travel, and Zero-Copy Cloning.
- Basic to intermediate Snowflake Cortex experience and understanding of LLMs, embeddings, text processing, and NLP.
- Experience with ELT/ETL tools such as dbt, Airflow, or Informatica.
- Knowledge of AWS, Azure, or GCP and data lake/lakehouse architecture.
- SQL proficiency, working Python knowledge, and experience with Git and CI/CD pipelines.
- Strong analytical problem-solving, communication, collaboration, and learning agility.
Preferred Qualifications
- SnowPro Core certification.
- Experience with AI-enabled data platforms.
- Exposure to vector databases or RAG architectures.
- Familiarity with Power BI, Tableau, or Looker.
- Understanding of DataOps or MLOps practices.
Deliverables and Success Metrics
Deliver scalable, reliable data pipelines and AI-enriched datasets using Snowflake Cortex.
Produce optimized queries, cost-efficient workloads, and high-quality AI-ready data models.
Success is measured by pipeline reliability, AI transformation adoption, query efficiency, cost optimization, data quality, and stakeholder satisfaction.
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