Driven data engineer with 3+ years of experience wrangling big datasets. Building robust data platforms that lay the groundwork for revealing game-changing analytical insights.

Conceptualized and implemented data pipelines to the stage, transform, and load structured and semi-structured data into systems for analytics.

Worked as a Data Analyst in building data models for Data Reporting, Data Visualization, and gathering meaningful insights for various clients.

Experience in various domains like Retail, Insurance, Mortgage & Finance.

✨ Skillset:

 👉 Programming languages: Python, Scala

 👉 Data processing: Spark

 👉 SQL: Redshift, Azure Kusto, MySQL, T-SQL, Athena 

 👉 Storage Files: Parquet, CSV, JSON, Excel

 👉 Reporting: Power BI, Tableau

 👉 Cloud providers: AWS, Azure

 👉 ETL Tools: Databricks, Azure Synapse Analytics, Azure Data Factory, AWS Glue, Snowflake.   

🛞 Serverless: AWS Lambda, Azure Functions

⚓ File Storage: AWS S3, Azure Data Lake Gen2

🫧 Core Competencies:

 👉Data Warehousing

 👉Designing ETL Pipeline Architecture

 👉Data Modeling

 👉Database Migration

 👉Business Intelligence

 👉Database Design

 👉Strategic Planning and Implementation

🌟 Achievements:

 👉Maintained data pipeline up-time of 95% while ingesting streaming and transactional data across 7 different primary data sources.

 👉Automated ETL processes across millions of rows of data, which reduced manual workload by 30% weekly.

 👉Optimized ongoing process of data transformations in Spark reducing execution time by 65% using best practices of distributed computing.

 👉Received positive client testimonies from Mortgage and Finance domains.

📧 Please feel free to reach me at prashantwitty@gmail.com to discuss any data-related solutions.