Vikram Adithya

Architected and optimized high-performance data engineering solutions for Adobe Experience Platform (AEP), Adobe CDP, and Real-Time CDP, enabling real-time customer activation and comprehensive Customer 360 views.

Led the development of enterprise-scale ETL/ELT pipelines using Databricks, Spark, Python, SQL, Delta Lake, ADLS, and AWS S3, processing large volumes of structured and semi-structured customer data.

Implemented robust Delta Lake architectures, leveraging ACID transactions, schema evolution, and incremental processing patterns to ensure data reliability and optimize storage for critical customer data.

Engineered scalable Spark/PySpark applications and Databricks notebooks, optimizing distributed data processing and complex transformations across large enterprise datasets for improved efficiency.

Collaborated with Adobe Architects, AJO Engineers, Marketing Technology teams, and business stakeholders to translate complex business requirements into scalable data architectures and solutions.

Engineered and optimized scalable data pipelines using Databricks, Spark, PySpark, Python, SQL, and Delta Lake, processing large volumes of customer, transaction, and behavioral data for enterprise initiatives.

Designed and maintained end-to-end ETL/ELT pipelines, ingesting data from diverse enterprise sources to create reliable datasets for analytics, customer profiles, and marketing applications.

Developed robust data processing workflows in Databricks using PySpark and SQL, implementing advanced data cleansing, transformation, and incremental processing for large-scale datasets.

Integrated and prepared customer data for unified profiles, identity resolution, segmentation, and real-time activation within Adobe Experience Platform (AEP) and Adobe CDP.

Designed and implemented scalable conceptual, logical, and physical data models for enterprise reporting and analytics, utilizing Databricks Lakehouse architecture to enhance data accessibility.

Developed robust Bronze, Silver, and Gold data layers with Databricks and Delta Lake, ensuring reliable and analytics-ready data products for downstream consumption.

Translated complex business and reporting requirements into scalable data structures and dimensional models, enhancing reporting and analytics capabilities.

Built and optimized ETL/ELT pipelines using AWS Glue, PySpark, Python, and SQL, efficiently processing large volumes of structured and semi-structured data for reporting.

Designed and developed scalable data models and ETL pipelines, supporting enterprise reporting, analytics, and business intelligence requirements for enhanced decision-making.

Constructed robust data pipelines using Apache Spark, PySpark, Hive, Python, and SQL, processing large volumes of structured and semi-structured data.

Managed and maintained Bronze, Silver, and Gold data processing layers, ensuring organized and reporting-ready datasets for various business needs.

Optimized large-scale data processing performance by applying data partitioning, bucketing, file-format optimization, and Spark tuning techniques.

Developed and maintained enterprise ETL pipelines and data integration workflows, enhancing reporting, analytics, and business intelligence capabilities.

Designed and implemented logical and physical data structures for analytical databases, aligning with business requirements to improve data accessibility.

Engineered ETL workflows using Informatica PowerCenter, SQL, Python, and Shell scripting to efficiently extract, transform, and load data from diverse source systems.

Improved overall data quality by implementing robust data cleansing, standardization, transformation, and business-rule validation processes.