Sneha Sinha

Lead ML Engineer

Bengaluru, Karnataka, India3 yrs 7 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in building scalable ELT/ETL pipelines.
Passionate about AI/ML and intelligent automation.
Proven track record in optimizing cloud data architectures.

Stackforce AI infers this person is a Data Engineering and Machine Learning specialist in the SaaS industry.

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Skills

Core Skills

MlopsData EngineeringCloud ComputingMachine Learning

Other Skills

Amazon Web Services (AWS)MLAI EngineerSQLSnowflakeAmazon RedshiftPython (Programming Language)snowparkData Warehouse Architectureci/cddockerGitBig Data AnalyticsLarge Language Models (LLM)Natural Language Processing (NLP)

About

Data Engineer || Open for work A data engineer with 2+ years of experience. I specialize in building scalable ELT/ETL pipelines, optimizing cloud data architectures, and automating end -to-end analytics workflows. I create solutions that are efficient, reliable, and cost-effective. I’m deeply passionate about the AI/ML , constantly researching, experimenting, and building LLM-powered POCs to integrate intelligent automation into modern data systems. Let’s connect if you're passionate about solving real-world problems with data! Reach out to me : sneha070600@gmail.com

Experience

3 yrs 7 mos

Total Experience

1 yr 6 mos

Average Tenure

7 mos

Current Experience

Paytm

Senior ML Engineer ( Data & ML Ops )

Oct 2025 – Present · 7 mos · Bengaluru, Karnataka, India

Amazon Web Services (AWS)MLOpsData EngineeringMLAI Engineer

Csg

2 roles

Data Engineer

Sep 2023 – Oct 2025 · 2 yrs 1 mo

Designed ELT workflows (10M+ rows/day) from S3 to Snowflake with layered architecture and integrated SNS-based delay/failure alerts.
Migrated Redshift to Snowflake for 5+ clients, improving query performance by 40% with optimized ingest, staging, and mart layers.
Audited S3 usage across 4+ clients, implemented lifecycle policies and class transitions, saving ~$500/month.
Built a Snowflake query recommendation model using Snowpark & Cortex LLM, leveraging metadata for intelligent SQL tuning.
Automated error tracking for 20M+ daily records, detecting spikes and ingestion failures to reduce load issues by 40%.
Refactored Redshift ETL workflows and enhanced OpsGenie alert logic for better error monitoring and stability.
Containerized ELT jobs with Docker, deployed via Kubernetes and CI/CD (GitHub Actions) for consistent, secure delivery.

SQLSnowflakeAmazon Web Services (AWS)Amazon RedshiftPython (Programming Language)snowpark+15