Developed microservices to interface with machine learning / deep learning models for automated and optimized retail pricing markups. Achieved a 7% increase in total GMV through optimized pricing strategies.

Currently architecting microservices for hosting shopping ads on Google at scale to target new customer acquisition and brand awareness.

Created efficient batch data processing pipelines (ETL) for retailer onboarding and analytics. Adopted Python, Golang, Apache Spark, Kafka, Airflow, Docker, kubernetes, Postgres, Redis, BigTable, Snowflake and REST APIs for streamlined operations.

Designed and implemented event-driven microservices to further enrich Shipt catalog data feeds in near real-time, reducing the end-to-end flight time by 40%.

Engineered end-to-end Apache Spark Structured streaming pipelines for capturing web page load latency metrics and provided valuable insight on user experience to enhance understanding and optimization.

Implemented ELT process to create materialized views in Snowflake representing conceptual User Journeys on the platform. Empowered the data science team to analyze user behavior patterns, utilizing the view as a key resource.

Crafted batch data pipelines, leveraging AWS Glue (Apache Spark, Python) and AWS Batch, to wrangle data from diverse internal and external sources.

Facilitated the marketing team in optimizing advertisement campaign portfolios. Designed data pipelines capturing performance metrics from various sources, including Google Ads, Facebook Ads, and Bing Ads.

Designed and implemented CI/CD processes for efficient deployment and testing of ETL jobs. Utilized AWS CloudFormation, CodePipeline, and AWS Lambda for streamlined automation.

Engineered automation for executing AWS Athena queries on the Glue catalog (S3 buckets - parquet data format) and sending daily reporting results to relevant stakeholders for informed decision-making.