Summary:
• Migrating data from SAP Data Warehouse to Azure Storage Gen2/Databricks Lakehouse (Unity Catalog), enabling seamless integration with PowerBI reports
• Providing leadership to the team and ensuring the successful execution of projects
• Ingest SAP tables through Azure Data Factory Pipeline and dispose in StarSchema arquitecture
• Supporting streaming data pipelines utilizing Spark Structured Streaming, Change Data Feed, and Delta Live Tables
• Explore View Materializing with Spark Streaming for incremental joins


Day-to-day responsibilities:
• Designing data pipeline architectures to ensure optimal performance
• Assisting the team in overcoming technical challenges and roadblocks
• Mentoring and guiding squad members in their career development
• Defining and promoting code patterns for reusable and maintainable code for Databricks and Data Factory

Improvements/Accomplishments:
• Created a generic framework that supports all data wrangling.
• Reduced computer costs by optimizing cluster usage/resources and applying Spark Structured Streaming
• Reduced cloud costs by migrating Data Factory parallelism to Databricks Jobs run in Spark Streaming pipelines

Technology Stack:
• Azure Data Factory, Azure Functions, Azure Storage Gen2
• Azure Databricks

Summary:
• Migrating data from RDS to S3 using EMR and Databricks, which involved Spark and Hadoop
• Establishing a data-serving mechanism from S3 through REST API, developed using Node.js and Express
• Creating a robust Data Pipeline leveraging Delta Lake libraries and Kafka

Day-to-day responsibilities:
• Processed historical data, optimizing performance and efficiency by coalescing thousands of small files and database dumps into a Delta Table using batch and stream processing, specifically Databricks Autoloader.
• Applied intricate business rules to the data, employing PySpark for data wrangling
• Supported an ongoing pipeline, adding new datasets with Scala, reading Kafka topics, applying deduplication, business rules, and saving them to S3
• Facilitated data access via a REST API developed with Node.js and Express
• Pioneered code patterns for reusability and maintainability

Improvements/Accomplishments:
• Creating a Historical pipeline responsible for ingesting more than 1 Petabyte of data, demonstrating my capability to handle large-scale data operations
• Developing reports for data quality, aiding in the identification of erroneous or missing data and rule changes
• Supporting the creation of monitoring tools to ensure the health of the data pipeline
• Reducing operational costs through cluster optimization and adhering to clean code practices

Technology Stack:
• Leveraging Java and Python for Blockchain and REST API collectors
• Employing Databricks pipelines powered by PySpark, Delta Lake, and Autoloader
• Crafting EMR pipelines using Spark and Scala
• Managing RDS with PostgreSQL databases
• Serving data through REST API on ECS with Node.js
• Facilitating data access via WebSockets using Python

This role not only showcased my proficiency in data engineering but also highlighted my problem-solving skills, adaptability, and commitment to maintaining data quality and pipeline efficiency.

(Part-time job)

Summary:
• Migrating data from SAP Data Warehouse to AWS Redshift or Databricks Lakehouse, enabling seamless integration with PowerBI reports
• Providing leadership to the team and ensuring the successful execution of projects
• Supporting streaming data pipelines utilizing Spark Structured Streaming, Change Data Feed, and Delta Live Tables


Day-to-day responsibilities:
• Designing data pipeline architectures to ensure optimal performance
• Assisting the team in overcoming technical challenges and roadblocks
• Mentoring and guiding squad members in their career development
• Defining and promoting code patterns for reusable and maintainable code

Improvements/Accomplishments:
• Created a generic framework that supports all data wrangling.
• Refactored the framework to run in AWS Glue, AWS EMR, and Azure Databricks
• Reduced computer costs by optimizing cluster usage/resources and applying Spark Structured Streaming

Technology Stack:
• AWS Glue, Redshift, Athena, AWS S3
• Azure Databricks