Mohit Garg

Strong leader and implementer with around 14+ years of cross functional and technical experience (Data, Infra, Application Development) in designing and implementing business intelligence solution for Financial, Ecommerce, Product(marketing domain), Service base, Entertainment sectors to drive strategic and tactical decisions.

Specialties: Data Platform, Application Development, Big Data, Cloud Computing Technology leadership, architecture, strategy planning, project management, Budget Management

• Leadership :- Team Leadership, Mentorship, Career Development, Performance Management, Conflict Resolution
• Management :- Vendor Contract Management, Budget Planning, OKR Management, Regulatory Reporting
• Development & Delivery :- Product Management, Project Management, Agile Methodologies, Sprint Planning, Stakeholder Engagement
• Analysis & Improvements :- Process Optimization, Feature Development, Reliability Enhancement, Decision-Making Analytics

• AWS Certified Solution Architect
• Coursera Machine Learning Specialization

• Best Performer in Adobe (Star, Team, Global Award)
• One of the Best performer in CRED

Ingestion & Analytics Platform (Funding Societies) :  A self service data platform, which take care of ingestion of almost 15-20 sources, building analytics models, data governance, data quality etc

• Ingesting Million of data points in a single day and Handling 1k+ DAG in a day
• Utilize Snowflake as a Data Warehouse for creating data models and analytics layers accessible to stakeholders without deep SQL expertise.
• Implement a domain-driven architecture encompassing RAW ingestion, data cleaning, common data modeling, and analytics layers.
• Develop a Data Catalog with business glossary, lineage, and data assets using Open metadata deployed on Kubernetes, providing organization-wide data catalog capabilities.
• Build a scalable data quality framework (Soda Core) to ensure consistent data quality maintenance.
• Build Analytics or Metrics layer which will help to perform self-serve analytics without worry about query
• Tech Stack :- Snowflake, Python, Airflow, Spark, Open Metadata, SodaCore, EKS, AWS Tech Stack

Metrics Store Platform (Meesho): Creating End To End Solution for Metrics Platform which will help to streamline all metrics definition across Org level, Impact is to reduce cost and streamline definition of metrics

• Provides a centralized view of business metrics, cuts, dimensions, segments, and metrics across different organizational units.
• Reduces compute costs by eliminating redundant metric computations, ensuring consistent metric definitions across organizations.
• Scale :- 1K+ DAG, 100 Tables, Billion of Events, Multi tenant System
• Tech Stack :- Databricks, Spark, Airflow, Python, Presto, SQL, EKS, Terraform, AWS

Streaming Platform (Meesho) :- It provides insights about in near real time. Vision is to move away from Enterprise solution (Mixpanel) to build in-house solution using open source tech stack.

• Impact: Improved real-time data analytics, enabling proactive decision-making and operational efficiency.
• Scale :- Billion of Events in Real Time, Kafka Connect
• Tech Stack :- Superset, Kafka Connect, Datalake, Pinot, Flink, EKS,Jenkins,AWS

Data Science Platform (Meesho) :- Develop a self-service Data Science Platform for model training, serving, and performance monitoring.

• Impact :- Accelerated machine learning workflows, improving model accuracy and deployment efficiency.
• Tech Stack :- MlFlow, Python, ECS, Sagemaker, Feature Store (Sagemaker), Build-InHouse Feature Store, AWS

Experimentation Platform (CRED) :- A/B Testing platform which will help users to run their different experiment as per north star & guardial metrics based upon different user/product segment

• Impact :- Growth in company revenue and User Engagement
• Tech Stack :- SpringBoot, Mysql, Microservice architecture, Databricks, Presto, Jenkins, AWS

Communication Platform (CRED) :- Generic Platform that helps to send whatsapp, email comms for different campaigns etc.

• Impact :- It help to increase revenue, user engagement on new features
• Tech Stack :- Dropwizard, Kafka, Rate Limiting, DynamoDB, Mysql, Spark, Microservice Architecture, MongoDB

Data Quality Platform (Adobe) :- Architect a self-service data quality framework providing data profiling, anomaly detection, and rule-based alerting.

• Impact :- Enhanced data quality management, ensuring reliable and accurate data for analytics and reporting.
• Tech Stack :- Spark, Kafka, EMR, Mysql, Elastic Search