Vikram Adithya

Designed and optimized high-performance data engineering solutions for customer profiles, segmentation, identity resolution, Customer 360, and real-time customer activation across the Adobe Experience Platform ecosystem.

Built enterprise-scale ETL/ELT pipelines using Databricks, Apache Spark, PySpark, Python, SQL, Delta Lake, Azure Data Lake Storage (ADLS), and AWS S3 to ingest, transform, cleanse, enrich, and publish large volumes of structured and semi-structured customer data.

Implemented Delta Lake architecture for reliable data storage and processing, leveraging ACID transactions, schema enforcement, schema evolution, time travel, optimized data layouts, and incremental processing patterns.

Developed and maintained Azure Data Factory pipelines for data ingestion, transformation, scheduling, and dependency management, integrating enterprise source systems with ADLS, Databricks, and downstream platforms.

Integrated streaming and event-driven customer data using Kafka/Event Streaming to support near-real-time ingestion, customer profile updates, segmentation, identity processing, and activation use cases.

Implemented robust data quality, validation, reconciliation, error handling, and monitoring mechanisms across ETL/ELT pipelines, ensuring accuracy, completeness, and reliability of critical customer data.

Utilized Unity Catalog within Databricks to support enterprise data governance, centralized access control, data discovery, permissions management, lineage, and secure management of data assets.

Developed ML feature engineering pipelines using Databricks, Spark/PySpark, Python, and SQL to prepare and manage machine-learning-ready customer features for analytics and predictive use cases.

Developed and maintained end-to-end ETL/ELT pipelines to ingest data from multiple enterprise sources and transform it into reliable datasets for analytics, customer profiles, segmentation, and downstream marketing applications.

Built data processing workflows in Databricks using PySpark and SQL, implementing data cleansing, transformation, aggregation, enrichment, and incremental processing logic for large-scale datasets.

Worked with Adobe Experience Platform (AEP), Adobe Customer Data Platform (CDP), and Real-Time CDP to prepare and integrate customer data required for unified profiles, identity resolution, segmentation, and activation.

Supported Adobe Journey Optimizer (AJO) initiatives by delivering accurate and timely customer datasets used for personalized customer journeys, audience targeting, and customer engagement use cases.

Designed and implemented customer data pipelines supporting Customer 360 capabilities by bringing together customer, purchase, interaction, digital, and behavioral data from multiple enterprise systems.

Developed batch and near-real-time data pipelines using Kafka/Event Streaming to process customer events and make data available for real-time activation and downstream applications.

Optimized Databricks and Spark workloads by reviewing partitioning strategies, joins, transformations, file sizes, and query execution to improve pipeline performance and processing efficiency.

Supported CI/CD implementation using Azure DevOps and Git, automating deployment of Databricks notebooks, PySpark code, SQL scripts, and ADF pipelines across environments.

Designed and implemented scalable conceptual, logical, and physical data models for enterprise reporting and analytics using Databricks Lakehouse architecture.

Developed Bronze, Silver, and Gold data layers using Databricks and Delta Lake to support reliable, reusable, and analytics-ready data products.

Translated business and reporting requirements into scalable data structures, dimensional models, fact tables, dimension tables, and curated reporting datasets, aligning with enterprise data architecture.

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

Applied Databricks and Delta Lake modeling best practices, including schema design, partitioning, incremental processing, data validation, and performance optimization, reducing reporting data latency.

Implemented data quality, reconciliation, and validation checks between source systems and curated Databricks datasets to ensure reporting accuracy.

Optimized Spark jobs, SQL queries, and Delta tables to improve data processing performance and reduce reporting data latency.

Supported enterprise reporting teams by creating trusted data marts and curated datasets consumed through Power BI and Tableau, maintaining data lineage and technical documentation.

Designed and developed scalable data models and ETL pipelines to support enterprise reporting, analytics, and business intelligence requirements.

Built data pipelines using Apache Spark, PySpark, Hive, Python, and SQL to process large volumes of structured and semi-structured data.

Developed and maintained Bronze, Silver, and Gold data processing layers to organize raw, cleansed, transformed, and reporting-ready datasets.

Implemented dimensional modeling concepts including fact and dimension tables, surrogate keys, business keys, and slowly changing dimensions (SCD).

Integrated data from relational databases, enterprise applications, files, and APIs into centralized Hadoop and cloud-based data platforms.

Developed ETL workflows using AWS Glue and Apache Airflow/Oozie for batch data ingestion, transformation, scheduling, and dependency management.

Migrated and optimized legacy data processing workflows into AWS S3, Redshift, and Databricks environments.

Applied data partitioning, bucketing, file-format optimization, and Spark tuning techniques to improve large-scale data processing performance.

Developed and maintained enterprise ETL pipelines and data integration workflows to support reporting, analytics, and business intelligence requirements.

Designed and implemented logical and physical data structures for analytical databases and reporting environments based on business requirements.

Developed ETL workflows using Informatica PowerCenter, SQL, Python, and Shell scripting to extract, transform, validate, and load data from multiple source systems.

Integrated data from Oracle, SQL Server, flat files, and other enterprise applications into centralized data warehouse and Hadoop environments.

Created reusable Informatica mappings, mapplets, workflows, sessions, and transformations for batch data processing.

Implemented data cleansing, standardization, transformation, and business-rule validation to improve overall data quality.

Worked with Hadoop, Hive, and Sqoop to ingest and process large datasets from relational databases into distributed data platforms.

Performed ETL performance tuning by optimizing SQL queries, Informatica workflows, data extraction strategies, and Hadoop processing jobs.