Parth Botadara

Big Data Engineer with over 3.5 years of experience in building scalable, cloud-native data solutions using Azure Databricks, Azure Data Factory, and PySpark. Skilled in processing high-volume datasets with tools like ELK Stack, OpenSearch, and SQL Server. Proficient in Python for data engineering, automation, and transformation tasks. Hands-on expertise in designing ETL pipelines, implementing data models, and optimizing analytics-ready datasets across Azure and AWS environments. Strong collaborator with a focus on data quality, performance optimization, and delivering business-impactful insights.

VC DataMart Frida Baby Data Insights Data Drill Food Chain ID

Developed and maintained an end-to-end data pipeline to transform raw data from multiple sources into business-ready datasets. Applied ELT principles to load, optimize, and validate data in SQL Server, enabling reporting and analytics through Power BI.

Key Skills:
MSSQL · Azure · Python · PySpark · Azure Data Factory (ADF)

Key Responsibilities:
• Designed and optimized ELT pipelines for large-scale data migration and transformation in Azure.
• Utilized PySpark for efficient processing of large datasets.
• Ensured data integrity and accuracy using validation logic during migration.
• Applied column back tracing techniques for reliable data mapping across systems.
• Collaborated with cross-functional teams to deliver business-aligned, analytics-ready data.

Managed end-to-end data processing for customer and order records with a focus on data quality, ETL automation, and business reporting. Enabled sales insights through data integration and transformation using ADF and PostgreSQL.

Key Skills:
PostgreSQL · Azure Data Factory (ADF) · Azure Blob Storage · NetSuite Saved Searches

Key Responsibilities:
• Designed and optimized PostgreSQL schemas for scalable customer and order data management.
• Created ADF pipelines to extract and transform data from NetSuite using Saved Searches.
• Prepared reporting datasets for Power BI dashboards to support business decision-making.
• Applied data cleansing and deduplication to ensure high-quality analytics.

Led a global data integration initiative to unify and analyze high-volume, high-frequency datasets from multiple internal and external sources, enabling cross-domain insights across partners, third parties, and subsidiaries.

Key Skills:
Python · SQL Server · Azure Data Factory (ADF) · Azure Databricks · Azure Services

Key Responsibilities:
• Built scalable ETL workflows using Python and ADF for ingesting diverse datasets.
• Improved data retrieval speed by 30% through pipeline optimization.
• Integrated multi-source datasets to support cross-domain analytics.
• Modeled and transformed data for BI and analytical consumption.
• Collaborated with stakeholders to deliver reliable, production-ready data solutions.
• Participated in Agile ceremonies, including sprint planning and reviews.

Led the Data Drill project to build scalable ETL pipelines for processing structured and semi-structured retail data from Amazon, Flipkart, and e-commerce platforms. Focused on batch processing, data ingestion, and forecasting.

Key Skills:
Python · MySQL · HBase · CSV/JSON/Parquet Ingestion · Power BI · Git

Key Responsibilities:
• Developed Python-based ETL workflows for ingesting data from multiple retail sources.
• Created batch processing scripts using Python and MySQL for high-volume data loads.
• Loaded multi-format data (CSV, JSON, Parquet) into HBase and MySQL for scalable access.
• Applied forecasting techniques to retail data for demand prediction.
• Prepared data models for reporting and dashboards in Power BI.

Designed scalable search and ingestion pipelines to enhance multilingual, regulatory-focused data search. Improved search performance, deduplication, and cross-identifier querying to drive customer satisfaction.

Key Skills:
Elasticsearch · ELK Stack · Kibana · Azure Databricks · Python · Azure Services

Key Responsibilities:
• Built custom Elasticsearch analyzers for synonyms, regex, and case-insensitive search.
• Developed a multilingual ranking model for improved global search relevance.
• Created ingestion pipelines with deduplication using unique identifiers.
• Built dynamic search templates supporting pagination, filtering, and multi-identifier queries.
• Enabled distinct search on nested fields using predefined logic.
• Collaborated with teams to deliver 20% improvement in customer satisfaction.
• Maintained production search pipelines ensuring high availability and reliability.

Microsoft Certified: Fabric Data Engineer Associate