Project Leader

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 & IntegrationDevelop 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 EnablementLeverage Snowflake Cortex functions within SQL for AI-powered transformations.Implement use cases such as:Text summarization of large datasets (logs, documents)Sentiment analysis and classificationNatural language enrichment of datasetsWork 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 ModelingDesign 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 OptimizationOptimize SQL queries and Snowflake workloads.Use clustering, caching, and efficient compute strategies.Monitor warehouse usage and control costs, including Cortex consumption. Data Quality & ValidationImplement data validation checks and quality frameworks.Ensure accuracy, completeness, and consistency of data.Debug and resolve pipeline and data issues. Security & GovernanceApply RBAC, masking policies, and row-level security.Ensure compliance with data governance and privacy standards.Maintain proper documentation and lineage for datasets. Collaboration & Agile DeliveryWork 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 TablesTime 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 basicsFamiliarity with:Prompt engineering techniquesSemantic 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.Good 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.