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.