BOOTS Walgreens Boots Alliance

Needs

BOOTS required data engineering, cloud solution architecture (GCP), and data science expertise for various SAP modules (C4/HANA, Commerce, Marketing, Analytics, Gigya, CRM, C4C, SAP CAR). This included building ML pipelines on GCP and developing Python-based AI solutions.

Solutions

A multi-cloud solution approach was adopted.

• Data Solution: Managed data engineers and solution architects for Azure and AWS cloud. Supported SAP C4/HANA, Commerce, Marketing, Analytics, Gigya, CRM, C4C, and SAP CAR (PMR, OPP). Developed ETL and data modeling for real-time data. Deployed Docker images and managed Datamart builds on GCP (Bigtable, Pub/Sub).

• Integration Solution: Developed REST APIs scaled on Google Cloud Hybrid APIGEE. Supported CI/CD (Jenkins, Github Tools, Spinnaker) for deployments.

• Data Science Solution: Developed proficiency in Marketing (Salesforce, SAP) and SAP Analytics with Google Cloud. Created Python programs using ML Deep Neural Networks (TensorFlow with BigQuery ML) for advanced SEO search wording (BERT). Applied AI for business problems. Created MATLAB program to export media assets to Google Cloud BigQuery and developed PoC prototypes in Python3/MATLAB.

Achievements

• Cloud Architecture & Data Engineering: Managed team for Azure/AWS cloud, supporting diverse SAP modules.

• GCP Machine Learning & AI: Developed ML pipelines on GCP, used TensorFlow/BigQuery ML for SEO, and Python/MATLAB for media asset analysis and PoCs.

• DevOps & API Development: Supported complex ETL, developed REST APIs on GCP Hybrid APIGEE, and managed CI/CD pipelines.

Benefits

• Advanced Analytics Capabilities: Leveraged GCP ML and AI to solve business problems and enhance SEO.

• Multi-Cloud SAP Expertise: Provided solutions across Azure, AWS, and GCP for SAP workloads.

• Efficient Data Operations: Streamlined data engineering, ETL, and DevOps processes.

• Innovative Prototyping: Developed PoCs for new data-driven solutions.