I am a highly skilled Data Engineer with over five years of experience in designing and implementing data solutions that transform how organizations utilize data. My expertise lies in big data technologies like Hadoop, Apache Spark, Apache Kafka, and NoSQL, alongside cloud platforms such as Databricks for scalable analytics and Salesforce Data Cloud for customer data management and integration.
I specialize in creating robust, scalable data architectures that meet the needs of diverse business environments. Whether it's building end-to-end data pipelines or real-time streaming frameworks, I bring a comprehensive skill set that ensures efficient data flow and insightful analysis. My goal is to help businesses harness the power of data by optimizing processes, reducing latency, and improving overall performance.
Key Skills & Expertise:
Hadoop & Apache Spark: Extensive experience in distributed data processing systems, including real-time and batch processing using Apache Spark. Proficient in using Spark Core, Spark SQL, and DataFrame APIs for large-scale data analytics.
Apache Kafka & Confluent Kafka: Built and maintained Kafka messaging systems for streaming data between various services and platforms, ensuring low-latency, real-time data handling.
Databricks: Expertise in Databricks for advanced analytics, ETL processes, and machine learning workflows. Created collaborative workspaces for team-based development and efficient big data processing.
Salesforce Data Cloud: Proficient in integrating customer data into Salesforce Data Cloud for enhanced analytics and customer engagement strategies, building a unified data view across multiple sources.
NoSQL Databases: Hands-on experience with databases like HBase and Cassandra for high-availability, low-latency applications where traditional databases may not perform as required.
Data Pipeline Development: Skilled in building data pipelines using Hadoop, Sqoop, Hive, HDFS, and Spark, automating the entire process to ensure data consistency and reliability.
Cloud Data Integration: Expertise in integrating on-premise data systems with cloud-based environments like Salesforce Data Cloud and using cloud storage solutions for scalable, secure data processing.
Shell Scripting & Automation: Proficient in automating repetitive tasks with shell scripts, improving workflow efficiency, and reducing manual intervention in data processes.
Project Highlights:
1. Enterprise Data Pipeline (Healthcare)
Tools/Technologies: Hadoop, Apache Spark, Scala, SQL, Databricks, Sqoop, Hive, Python
Description: Designed a comprehensive data pipeline to extract and process data from multiple relational databases, transform it using Apache Spark, and store it in Hive for reporting and analytics. Databricks was used for advanced analytics and performance improvements.
Responsibilities: Built and optimized Spark jobs using Scala and Python, automated data ingestion and transformation processes, and improved reporting systems.
2. Salesforce Data Cloud Integration for Customer Analytics
Tools/Technologies: Salesforce Data Cloud, Python, SQL, Shell Scripting
Description: Integrated multiple customer data sources into Salesforce Data Cloud, providing a unified view for better customer engagement and insights. Managed data migration and synchronization to ensure accurate and timely customer data.
Responsibilities: Developed custom data workflows for integrating and syncing data, built scripts for data transformation, and ensured data quality in Salesforce Data Cloud.
3. Real-Time Data Streaming with Kafka
Tools/Technologies: Apache Kafka, Hadoop, Spark, Hive, Shell Scripting, Java, Scala
Description: Implemented a real-time streaming solution using Apache Kafka to handle live data from various sources. The data was processed in real-time using Spark and stored in Hive for further analysis and reporting.
Responsibilities: Developed Kafka consumers, managed real-time data ingestion pipelines, and automated the entire process to minimize manual handling and ensure continuous data flow.
4. Home Automation System
Tools/Technologies: Hadoop, MapReduce, Java, HDFS, Hive
Description: Built a system to control electronic devices via a mobile app, processing data from IoT devices using custom MapReduce jobs. The data was stored in Hive for analytics and system optimization.
Responsibilities: Designed real-time data flows from IoT devices, optimized system performance, and ensured data availability for analysis.
5. Data Integration for Downstream Teams
Tools/Technologies: Hadoop, Apache Spark, Kafka, Scala, Python, SQL, HQL
Description: Developed a solution to extract and distribute real-time data from Kafka topics into Hive tables, enabling downstream teams to analyze updated information for business decisions.
Responsibilities: Built Kafka consumers, automated data pipeline workflows using Spark and shell scripting, and ensured consistent data updates for downstream processes.
Python
