Over four years of experience as Data Engineer with proficient in building and maintaining robust data pipelines for processing large-scale structured and unstructured data.
• Proficient in Cloud Technologies with extensi…
Over four years of experience as Data Engineer with proficient in building and maintaining robust data pipelines for processing large-scale structured and unstructured data.
• Proficient in Cloud Technologies with extensive experience in AWS services such as EC2, S3, Lambda, Glue, Athena, and Redshift. Skilled in utilizing Apache Spark and Apache Kafka for data processing and streaming.
• Proven expertise in ETL and Data Pipeline Management, leveraging Apache Airflow to orchestrate complex ETL processes for efficient data extraction, transformation, and loading.
• Expert in data acquisition, validation, and modeling (predictive and statistical), with strong data modeling and visualization skills. Proficient in creating impactful visualizations using tools like Power BI and Tableau to support business decision-making.
• Experienced in Azure (Data Lake, Data Factory, Databricks, Synapse Analytics) and Google Cloud Platform (BigQuery, Cloud Composer/Airflow, Dataflow/Data Fusion) for comprehensive data engineering solutions.
Designed and implemented ETL workflows using Apache Spark and Python, resulting in a significant reduction in data processing time and improved accuracy.
Developed a Power BI dashboard to visualize key business KPIs, streamlining reporting processes and saving 2 hours weekly.
Led the execution of data engineering projects annually, leveraging Python, PySpark, Apache Airflow, Databricks, Redshift, Snowflake, and AWS to ensure robust and scalable data pipelines.
Utilized Spark SQL for efficient loading of 10 tables into HDFS, achieving sub-3-second query response times.
Implemented AWS Data Pipeline workflows to optimize data extraction, transformation, and loading, reducing processing time by 10%.
Established real-time analytics capabilities in Snowflake, enabling agile decision-making for business stakeholders.
Spearheaded the optimization of ETL workflows using Apache Spark and Python, achieving a 30% reduction in data processing time.
Implemented performance enhancements in Snowflake, including materialized views and data masking techniques, resulting in 25% faster queries and improved data security.
Managed and automated ETL pipelines using AWS Glue, ensuring seamless data ingestion and transformation across complex data environments.
Enhanced PySpark scripts to streamline data ingestion from various sources, reducing execution time by 20% while maintaining data integrity.
Deployed Apache Spark workloads on Databricks, realizing a 30% cost reduction compared to on-premise clusters through efficient resource management.
Optimized Spark Streaming processes for real-time data processing from Kafka, integrating state-of-the-art transformations for actionable insights.
Implemented automated data processing workflows on AWS Athena, enhancing scalability and processing efficiency by 24%.
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