Sr. Snowflake Developer

HCLTech; Hyderabad, Bengaluru; Hybrid; INR 4,500,000 - 6,500,000; Full time

Job summary

HCLTech is hiring a Senior Snowflake Developer for PAN India with 7+ years in engineering and 4+ years hands-on Snowflake production. You will work across Snowflake architecture, data platforms, and cloud integrations, delivering end-to-end optimizations and POCs.

Join a team that designs scalable data solutions, leverages Snowflake Cortex, and collaborates with backend and AI/ML squads to drive value for customers.

Qualifications

• 7+ years of engineering experience; 4+ years hands-on Snowflake in production.
• Snowflake architecture expertise - three-layer, virtual warehouses, micro-partitions, clustering/pruning.
• Snowflake platform capabilities - Snowpark, Streams, Tasks, Dynamic Tables, Materialized Views.
• Account administration - RBAC, warehouses, auto-suspend/resume, replication.
• Advanced query tuning and cost governance, including AISQL/Cortex and credit optimization.
• Python (Snowpark), JavaScript/Python UDFs and stored procedures; cloud IAM and storage integrations.
• Strong fundamentals in data structures, algorithms, distributed systems, and large-scale design.
• SnowPro Advanced certifications and experience with Docker, Kubernetes, Terraform, CI/CD.

Responsibilities

• Design and implement Snowflake optimization capabilities for production accounts.
• Build POCs to validate ideas and demonstrate customer value.
• Collaborate with backend, AI/ML, and data engineering teams to ship features end-to-end.
• Translate Snowflake expertise into product capabilities and safe automated actions.

Skills

Snowflake

Big data concepts

Distributed systems

Tools

Snowflake

DBT

Python

Docker

Kubernetes

Terraform

CI/CD

AWS

Azure

GCP

Job description

Hi,

We are hiring for immediate joiner for Sr. Snowflake Developer for PAN India. Interested candidates please share your resume with below details.

Total & Relevant Experience:

Current & Expected CTC:

Notice Period:

Current & Preferred Location:

Skills: Snowflake, DBT & Python

Experience: 7+ Yrs

Mandatory

7+ years of engineering experience; 4+ years hands-on Snowflake in production.

• Snowflake architecture expertise - three-layer, virtual warehouses, micro-partitions, clustering/pruning, result/metadata/warehouse caching, time travel, fail-safe, and zero-copy cloning.
• Snowflake platform capabilities - Snowpark, Streams, Tasks, Dynamic Tables, Materialized Views, Search Optimization Service, Query Acceleration Service, Resource Monitors, Snowflake Optima, Query Insights, external functions, and SQL API.
• Account administration - RBAC, warehouses, Adaptive Compute, Gen2 Warehouses, multi-cluster policies, auto-suspend/resume, replication, SnowSQL/REST APIs.
• Advanced query tuning - Query Profile analysis, clustering keys, materialized views, Search Optimization.
• Cost optimization warehouse right-sizing , credit usage analysis, workload-based optimization, AISQL / Cortex cost governance (tagging, attribution, budget controls).
• Python (Snowpark), JavaScript/Python UDFs and stored procedures.
• Cloud platforms (AWS / Azure / GCP) - IAM, networking, storage integrations (S3/ADLS/GCS), PrivateLink, cloud-native Snowflake integrations.
• Strong fundamentals in data structures, algorithms, distributed systems, and large-scale system design
• Snowflake Cortex -Cortex AI Functions (AISQL), AI-driven optimization (intelligent recommendations, anomaly detection), and native Snowflake automation complementing optimization logic.
• FinOps practices and cost-attribution models for data platforms.
• Observability tools (Prometheus, Grafana, OpenTelemetry, Datadog).
• SnowPro Advanced certifications (Architect, Data Engineer, or Administrator).
• Experience with Docker, Kubernetes, Terraform, and modern CI/CD pipelines
• Design and build Snowflake optimization capabilities within DCO - warehouse sizing, query performance, clustering, materialized views, storage tiers, and credit consumption.
• Translate Snowflake expertise into product capabilities - detection rules, recommendation engines, and safe automated actions for production accounts.
• Build POCs to validate optimization ideas, demonstrate customer value, and support product discovery and pre-sales.
• Partner with backend, AI/ML, and data engineering teams to ship features end-to-end.