Job Title: Snowflake Developer (Data Lake Program with Snowflake Cortex)

Role Overview

The Snowflake Developer is responsible for building and optimizing data pipelines and analytical datasets within a Snowflake-based data lakehouse. This role also includes leveraging Snowflake Cortex to enable AI-driven data transformations, natural language processing, and intelligent data applications directly within the data platform.

Key Responsibilities

- Data Ingestion & Integration
- Develop and maintain scalable data ingestion pipelines (batch and near real-time).
- Load data from databases, APIs, files, and streaming systems into Snowflake.
- Handle structured and semi-structured data (JSON, Parquet, Avro, CSV).
- Integrate Snowflake with cloud storage platforms (S3, Azure Data Lake, GCS).
- Data Transformation (ELT)
- Build ELT pipelines using Snowflake SQL and native features (Streams, Tasks, Dynamic Tables).
- Implement transformations across Bronze, Silver, and Gold layers.
- Use tools like dbt for modular, reusable, and testable transformations.
- Snowflake Cortex & AI Enablement
- Leverage Snowflake Cortex functions within SQL for AI-powered transformations.
- Implement use cases such as:
- Text summarization of large datasets (logs, documents)
- Sentiment analysis and classification
- Natural language enrichment of datasets

- Work with vector embeddings and similarity search for semantic use cases.
- Assist in building AI-ready datasets for downstream analytics and ML.
- Collaborate with data scientists to integrate AI/ML logic into Snowflake pipelines.
- Support simple prompt engineering within Cortex functions for optimized outputs.




- Data Modeling
- Design and implement analytical data models (star schema, snowflake schema).
- Build fact and dimension tables optimized for BI and AI use cases.
- Ensure datasets are structured for both analytics and AI consumption.
- Performance & Cost Optimization
- Optimize SQL queries and Snowflake workloads.
- Use clustering, caching, and efficient compute strategies.
- Monitor warehouse usage and control costs, including Cortex consumption.
- Data Quality & Validation
- Implement data validation checks and quality frameworks.
- Ensure accuracy, completeness, and consistency of data.
- Debug and resolve pipeline and data issues.
- Security & Governance
- Apply RBAC, masking policies, and row-level security.
- Ensure compliance with data governance and privacy standards.
- Maintain proper documentation and lineage for datasets.
- Collaboration & Agile Delivery
- Work with architects, analysts, and AI/ML teams.
- Translate business and AI use cases into technical implementations.
- Participate in Agile development processes.

Required Skills & Qualifications

Core Snowflake Skills

- Strong hands-on experience with Snowflake.
- Advanced SQL skills with performance tuning.
- Experience with Snowflake features:
- Streams, Tasks, Dynamic Tables




- Time Travel, Zero-Copy Cloning

Snowflake Cortex & AI Skills

- Basic to intermediate experience with Snowflake Cortex functions.
- Understanding of:
- Generative AI concepts (LLMs, embeddings)
- Text processing and NLP basics

- Familiarity with:

- Prompt engineering techniques
- Semantic search and vector similarity concepts

Data Engineering Skills

- Experience with ELT/ETL tools (dbt, Airflow, Informatica).
- Knowledge of cloud platforms (AWS / Azure / GCP).
- Familiarity with data lake/lakehouse architecture.

Programming & Tools

- Proficiency in SQL and working knowledge of Python.
- Experience with Git and CI/CD pipelines.

Soft Skills

- Strong analytical and problem-solving abilities.
- Valuable communication and collaboration skills.
- Willingness to learn and adapt to AI-driven data technologies.

Preferred Qualifications

- Snowflake certification (SnowPro Core).
- Experience working on AI-enabled data platforms.
- Exposure to vector databases or RAG architectures.
- Familiarity with BI tools (Power BI, Tableau, Looker).
- Understanding of DataOps / MLOps practices.

Key Deliverables

- Scalable and reliable data pipelines.
- AI-enriched datasets using Snowflake Cortex.
- Optimized queries and cost-efficient workloads.
- High-quality, analytics- and AI-ready data models.

Success Metrics

- Pipeline reliability and performance.
- Adoption of AI-powered data transformations.
- Query efficiency and cost optimization (including Cortex usage).
- Data quality and stakeholder satisfaction.

📌 Project Leader (Noida)
🏢 Axtria
📍 Noida