Senior Data Engineer with 3+ years of experience building scalable cloud-native data platforms, distributed ETL/ELT pipelines, and high-volume analytics systems using AWS, Snowflake, Databricks, and Spark technologies. E…
Senior Data Engineer with 3+ years of experience building scalable cloud-native data platforms, distributed ETL/ELT pipelines, and high-volume analytics systems using AWS, Snowflake, Databricks, and Spark technologies. Experienced in designing and optimizing batch and streaming data pipelines, implementing secure data workflows, and supporting enterprise-scale data platforms processing large volumes of structured and semi-structured data.
Strong hands-on expertise in Python, SQL, PySpark, Snowflake, AWS cloud services, Kafka, Airflow, and modern data warehousing concepts. Skilled in building reliable data ingestion frameworks, CDC pipelines, operational monitoring systems, and cloud-based analytics solutions with a focus on scalability, observability, and performance optimization.
Experienced collaborating with cross-functional engineering teams to support integrations, automate workflows, improve data quality, and deliver analytics-ready datasets. Familiar with data governance principles, secure data handling practices, CI/CD pipelines, and modern cloud data ecosystems including Databricks and Snowflake
Designed and implemented end-to-end data pipelines using AWS Glue, PySpark, and EMR, processing high-volume financial data.
Built real-time streaming pipelines using Apache Kafka and Spark Structured Streaming for fraud detection and transaction monitoring.
Implemented Change Data Capture (CDC) pipelines using Debezium and Kafka, enabling near real-time data replication.
Developed and optimized Delta Lake architecture in Databricks for ACID-compliant data lakes.
Created Delta Live Tables pipelines to automate data ingestion, transformation, and validation.
Orchestrated workflows using Apache Airflow, ensuring reliable scheduling and monitoring of ETL jobs.
Designed scalable data models using SCD Type 1 & Type 2 techniques for historical tracking.
Built secure and high-performance data warehouse solutions using Snowflake and Redshift.
Optimized Spark jobs through partitioning, caching, and query tuning, improving performance by 40%.
Automated data workflows using AWS Lambda and Step Functions, reducing manual effort.
Collaborated with business stakeholders to deliver analytics-ready datasets and data marts.
Implemented data quality frameworks and monitoring dashboards for pipeline reliability.
Designed and implemented end-to-end data pipelines using AWS Glue, PySpark, and EMR, processing high-volume financial data., Built real-time streaming pipelines using Apache Kafka and Spark Structured Streaming for fraud detection and transaction monitoring., Implemented Change Data Capture (CDC) pipelines using Debezium and Kafka, enabling near real-time data replication., Developed and optimized Delta Lake architecture in Databricks for ACID-compliant data lakes., Created Delta Live Tables pipelines to automate data ingestion, transformation, and validation., Orchestrated workflows using Apache Airflow, ensuring reliable scheduling and monitoring of ETL jobs., Designed scalable data models using SCD Type 1 & Type 2 techniques for historical tracking., Built secure and high-performance data warehouse solutions using Snowflake and Redshift., Optimized Spark jobs through partitioning, caching, and query tuning, improving performance by 40%., Automated data workflows using AWS Lambda and Step Functions, reducing manual effort., Collaborated with business stakeholders to deliver analytics-ready datasets and data marts., Implemented data quality frameworks and monitoring dashboards for pipeline reliability.
Developed scalable ETL pipelines using Azure Data Factory (ADF) and Azure Databricks.
Built data lake solutions on ADLS Gen2, supporting structured and semi-structured data ingestion.
Implemented Delta Lake for reliable and efficient data storage with ACID transactions.
Developed batch and streaming pipelines using PySpark and Spark Structured Streaming.
Integrated CDC pipelines to capture incremental data changes from relational databases.
Designed and implemented data models in Azure Synapse Analytics for reporting and BI use cases.
Automated workflows and scheduling using ADF triggers and Apache Airflow.
Performed data transformation, cleansing, and enrichment for enterprise datasets.
Improved pipeline efficiency through query optimization and resource tuning.
Collaborated with cross-functional teams in Agile environments for timely delivery.
Supported CI/CD pipelines using Azure DevOps and Git for automated deployments.
Developed scalable ETL pipelines using Azure Data Factory (ADF) and Azure Databricks., Built data lake solutions on ADLS Gen2, supporting structured and semi-structured data ingestion., Implemented Delta Lake for reliable and efficient data storage with ACID transactions., Developed batch and streaming pipelines using PySpark and Spark Structured Streaming., Integrated CDC pipelines to capture incremental data changes from relational databases., Designed and implemented data models in Azure Synapse Analytics for reporting and BI use cases., Automated workflows and scheduling using ADF triggers and Apache Airflow., Performed data transformation, cleansing, and enrichment for enterprise datasets., Improved pipeline efficiency through query optimization and resource tuning., Collaborated with cross-functional teams in Agile environments for timely delivery., Supported CI/CD pipelines using Azure DevOps and Git for automated deployments.
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