(01) — Index·Data and AI Engineer·Hyderabad, IN

Alin Verma

Data and AI engineering, built to run in production.

I'm a data engineer at Data Mavericks. Most of my day is event-driven ingestion on AWS, ELT in Snowflake, and the unglamorous work that keeps ML systems trustworthy. Background in applied deep learning (computer vision, biometric fraud detection) from research stints at Samsung and UIDAI.

Selected work Get in touch

Currently: Data Engineer at Data Mavericks
Stack: AWS · Snowflake · Python · SQL
Elsewhere: LinkedIn ↗GitHub ↗

02Selected Work

Shipping soon.

Projects ship at <project>.alinverma.com as separate Vercel deploys. The first few land here.

—In flight

First deploys land here.

Projects ship as their own Vercel deployments at <project>.alinverma.com. Each one is independently developed, deployed, and linked here when it's live — rather than padding this section with case studies of work that isn't.

Card opens the deployment in a new tab
One subdomain per project, kept small and shippable
Until then: the experience and certifications below speak for themselves

03Experience

Where I've put hours in.

Three roles, oldest at the bottom. Each line is a specific thing I owned, not a job description.

Mar 2024—PresentFull-time
Data Engineer
Data Mavericks·Hyderabad
- Designed and shipped data ingestion and analytics on AWS — Kinesis, Lambda, S3, Glue, API Gateway — with data landing in Snowflake for batch and real-time workloads.
- Standardized Snowflake ELT pipelines and AWS event-driven architectures, improving pipeline reliability and downstream analytics consumption.
- Acted as the technical advisor on AWS and Snowflake architecture decisions — scalability, reliability, security, and cost-aware design.
- Built AWS-to-Snowflake pipelines feeding AI/ML systems and informed roadmap discussions for internal data platform improvements.
AWSSnowflakeKinesisLambdaS3GlueAPI Gateway
May 2023—Jul 2023Internship
Data Science Intern
UIDAI Technology Centre·Bangalore
- Built and evaluated machine learning pipelines for biometric fraud detection using supervised and deep learning techniques.
- Experimented with CNN-based fingerprint models to improve anomaly detection, evaluated against baseline classifiers.
- Implemented end-to-end ML workflows in Python — TensorFlow, OpenCV, scikit-learn.
PythonTensorFlowOpenCVscikit-learnCNNDeep Learning
Dec 2022—Aug 2023Internship
Research Intern
Samsung Research Institute·Remote · Bangalore
- Developed computer vision pipelines for pose estimation and keypoint detection using deep learning frameworks.
- Built tooling that streamlined annotation, labeling, and validation workflows for the research team.
- Translated experimental research models into reusable ML components for downstream use.
Deep LearningCNNPythonPose EstimationComputer Vision

04Credentials

Verifiable and current.

Three certifications, all in date. Click through to verify on the issuer's site.

DEA-C01

AWS Data Engineer — Associate

Amazon Web Services2024 – 2027

Verify ↗

DEA

SnowPro Advanced: Data Engineer

Snowflake2025 – 2027

Verify ↗

COF-C02

SnowPro Core Certified

Snowflake2024 – 2026

Verify ↗

05What I do

Four areas, one practice.

The shape of my day: pulling data, modeling it, putting models behind it, and making the whole thing safe to change.

AWS

Cloud-native data infrastructure and managed services.

Cloud-native architecture, distributed data systems, and managed services
Event-driven ingestion, API-based integrations, and infrastructure automation
Designing scalable, reliable, and secure cloud data platforms

Snowflake

Warehousing architecture and AI-driven analytics.

Data warehousing architecture (Medallion Architecture)
ELT pipelines for batch and real-time loads
Cortex AI Agents and Snowflake Intelligence

AI / ML

Applied ML and deep learning for real-world data problems.

Applied machine learning and deep learning for real-world data problems
Building ML-ready data pipelines and feature preparation workflows
TensorFlow, OpenCV, scikit-learn

Data Engineering

Pipelines built to run reliably in production.

ETL/ELT pipeline design across batch and real-time workloads
Schema standardization and reliability-focused data engineering practices
SQL, Python, and orchestration patterns for cloud-native pipelines

The toolbelt

PythonSQLAWSSnowflakeLambdaKinesisS3GlueAPI GatewayTensorFlowOpenCVscikit-learn

06Get in touch

One form, one inbox.

No public email or phone. Drop a note here and it lands directly with me.