Project LEad

Position Summary

To be a driven business analyst who can work on complex Analytical problems and help the customer in better business decision making especially in the area of pharma/life sciences (domain).

Job Responsibilities

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

1. 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).

2. 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.

3. 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.

4. 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.

5. 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.

6. Data Quality Validation

• Implement data validation checks and quality frameworks.
• Ensure accuracy, completeness, and consistency of data.
• Debug and resolve pipeline and data issues.

7. 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.

8. 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.

Education

BE/B.Tech

Master of Computer Application

Work Experience

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

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.
• 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.

Behavioural Competencies

Ownership

Teamwork Leadership

Cultural Fit

Motivation to Learn and Grow

Technical Competencies

Problem Solving

Lifescience Knowledge

Communication

Capability Building / Thought Leadership

Skills