# Snowflake Engineer (AI & Data Cloud)

Experience Level: 5+ Years

Location: Chennai

Type: Full-time

### Position Overview

We are seeking a highly skilled and forward-thinking Senior Snowflake Data Engineer with 5 years of data engineering experience to design, build, and optimize our next-generation enterprise data platform.

In this role, you will go beyond traditional data warehousing. You will lead the development of performant data pipelines, deploy native machine learning and generative AI workflows directly inside the data cloud using Snowflake Cortex AI, enforce enterprise-grade data security via Snowflake Horizon, and champion advanced query performance tuning.

### Key Responsibilities

Data Pipeline & Platform Architecture: Design, build, and maintain scalable, robust batch and real-time data ingestion pipelines using Snowflake native capabilities (Streams, Tasks, Snowpipe, Snowpipe Streaming, and `COPY INTO`).

Native AI & ML Engineering: Implement generative AI, Retrieval-Augmented Generation (RAG) pipelines, and intelligent search functions inside Snowflake using Cortex AI Services (e.g., `Cortex Search`, `Cortex Analyst`, and `AI_COMPLETE` LLM functions).

Query Optimization & FinOps: Perform deep performance tuning, query profiling, and cluster configuration optimization. Manage computing costs by effectively engineering search optimization services, clustering keys, materialized views, and right-sizing virtual warehouses.

Data Governance & Compliance: Set up end-to-end data security and observability frameworks utilizing Snowflake Horizon. Enforce tag-based dynamic data masking, row-access policies, data classification profiles, and system Data Metric Functions (DMFs) for proactive data quality monitoring.

Advanced Programmability (Snowpark): Develop sophisticated data processing,



feature engineering pipelines, and custom logic using Snowpark (Python/SQL) rather than traditional external compute engines.

Collaboration & AI Coding Integration: Champion modern developer tools like Snowflake CoCo (Cortex Code) and Git integrations to accelerate deployment cycles, write clean code, and manage schema changes seamlessly.

### Required Core Technical Skills (Snowflake-Specific)

Core Snowflake Mastery: Deep architectural knowledge of Snowflake s multi-cluster shared-data structure (Storage, Compute, and Cloud Services abstraction). Expertise with micro-partitioning, zero-copy cloning, and Time Travel/Fail-safe behaviors.

Snowflake Cortex AI: Proven hands-on experience utilizing serverless Cortex LLM functions (`AI_EXTRACT`, `AI_GENERATE`, `AI_REDACT`) and constructing vector embeddings (`AI_EMBED`) for semantic search architectures.

Snowflake Horizon & Governance: Experience creating and deploying advanced security policies (Dynamic Data Masking, Row-Level Security, Object Tagging) and tracking data lineage via `ACCOUNT_USAGE` or Horizon Context tools.

Advanced SQL & Query Tuning: Master-level proficiency in writing complex, highly optimized SQL. Expert capability in reading query profiles, identifying data spills, resolving queuing bottlenecks, and fixing fan-out joins.

Here is the updated section for Highly Desirable / "Nice to Have" Skillsincluding the cloud platform experience (AWS, GCP,



or Azure) alongside the Snowpark and ML requirements.

### Highly Desirable / "Nice to Have" Skills

Cloud Ecosystem Experience (AWS, GCP, or Azure):Strong familiarity with at least one major cloud provider architecture (e.g., AWS, Google Cloud Platform, or Microsoft Azure). Hands-on experience managing underlying cloud infrastructure that interacts with Snowflake, such as:

Configuring storage buckets (AWS S3, Google Cloud Storage, or Azure Blob/ADLS Gen2) and establishing secure External Stages.

Setting up secure network plumbing and IAM access integrations (e.g., AWS IAM roles, Azure service principals, or GCP service accounts).

Managing data movement across cloud boundaries or leveraging cloud-native messaging systems (like AWS SQS, Azure Event Grid, or GCP Pub/Sub) for real-time ingestion pipelines.

Snowpark Expertise:Robust programming background using Snowpark Pythonfor data frame manipulation, creating User-Defined Functions (UDFs), and building stored procedures.

Machine Learning (ML) Foundations:Solid understanding of ML engineering paradigms, including feature generation, data preprocessing, and model deployment (experience with Snowflake ML functions or external model deployment is a huge plus).

Modern Data Stack Toolkit:Practical experience pairing Snowflake with ecosystem tools such as dbt(data build tool), Apache Iceberg tables, or orchestration tools like Apache Airflow.

### Qualifications

Experience: Minimum of 5 years of professional data engineering experience, with at least 3 years explicitly dedicated to building solutions on top of the Snowflake Data Cloud platform.

Certifications (Preferred): Snowpro Core, Snowflake Certified Data Engineer are strongly preferred.

📌 Snowflake Engineer (AI & Data Cloud) (Chennai)
🏢 Velodata Global
📍 Chennai