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We are hiring for-
Snowflake Developer
Experience: 6-8 Years
Location: Location: Bangalore, Gurugram
Work Mode: Hybrid
Notice Period: Immediate / Short Notice Preferred
Job Description — Senior Snowflake Developer / Data Engineer
Role Summary
We are seeking a Senior Snowflake Developer / Data Engineer with 6–8 years of experience to design, build, and automate data pipelines on Snowflake, with a strong emphasis on Python-driven orchestration and integrating AI/LLM components into automated workflows. This role goes beyond writing SQL — it requires the ability to engineer end-to-end automation flows that connect data platforms, AI/ML components, and orchestration tooling to solve data movement, transformation, and migration problems at scale.
Key Responsibilities:
Design and build automated data pipelines on Snowflake using Python, connecting ingestion, transformation, and validation stages into a cohesive, repeatable flow
Integrate AI/LLM components (e.g., Snowflake Cortex, LLM APIs, or similar AI services) into data pipelines to automate tasks such as code conversion, data classification, anomaly detection, or validation
Architect and implement medallion-style (Bronze/Silver/Gold) data layers, with automated transformation logic rather than manual, one-off scripting
Build orchestration and monitoring around automated pipelines (retry logic, error handling, logging, alerting) so pipelines run reliably with minimal manual intervention
Evaluate where automation can replace or accelerate manual data engineering effort, and build the tooling to do so
Troubleshoot and resolve failures in automated pipelines, including cases where AI-generated output requires refinement or correction logic
Continuously improve pipeline accuracy and efficiency by feeding observed failure patterns back into the automation logic itself, not just fixing individual outputs
Collaborate with data architects, analysts, and stakeholders to translate data movement/migration requirements into automated, scalable solutions
Document pipeline design, automation logic, and validation approach for team and stakeholder visibility
Required Skills & Experience:
Experience level
6–8 years of overall data engineering experience, with significant hands-on Snowflake development
Snowflake platform
Strong hands-on expertise: virtual warehouses, Snowpipe/Snowpipe Streaming, Streams & Tasks, Time Travel, RBAC and masking policies
Experience with Snowflake Cortex (AI/LLM functions) or demonstrated ability to integrate external AI/LLM services with Snowflake
Strong SQL performance tuning (query profiling, clustering, materialized views/dynamic tables)
Python & automation
Strong Python skills, specifically for building automation/orchestration pipelines — not just scripting, but designing pipelines that chain multiple components together reliably
Experience connecting AI/ML components (LLM APIs, ML models, or AI-powered services) into a broader automated workflow
Familiarity with orchestration frameworks or patterns (e.g., Airflow, Dagster, custom orchestration, or Snowflake-native Tasks/Streams) for scheduling and sequencing pipeline stages
Comfortable building error-handling, retry, and monitoring logic around automated processes
Data engineering fundamentals
Strong SQL across at least one traditional RDBMS (SQL Server, Oracle, PostgreSQL, etc.) in addition to Snowflake
Experience with ETL/ELT pipeline design, data validation, and reconciliation approaches
Understanding of data platform migration patterns — moving data and logic from a legacy system to a modern cloud data platform
Working style
Comfortable operating as a builder of automation, not just a consumer of tools — genuinely enjoys engineering the pipeline itself
Able to work independently on ambiguous problems and design a solution architecture, not just execute a predefined task list
Strong communication skills to explain automation design choices and trade-offs to both technical and non-technical stakeholders
Preferred / Nice-to-Have
Experience with AI-assisted or automated code conversion tooling (e.g., SnowConvert, BladeBridge, or similar)
Experience building custom automation that combines multiple AI/LLM calls in sequence (agentic-style workflows)
Background in a regulated or data-sensitive industry (financial services, healthcare, insurance)
Contributions to internal tooling, accelerators, or reusable automation frameworks at a previous employer
Exposure to modern BI/analytics tools for downstream reporting integration
Success Metrics for This Role
Automated pipelines built are reliable, maintainable, and require minimal manual intervention over time
Demonstrated reduction in manual effort through automation, measurable against a defined baseline
AI/LLM integrations perform accurately and their failure modes are well understood and handled
Pipeline design and automation logic are clearly documented and can be maintained by others
Interested candidates please share your resume at
shruthim@qusonic.com
Work arrangement
No
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