Prashanth Reddy Nallaralla

Senior Data Engineer @ Sunnybrook

About

Results-driven Data Engineer with 6+ years of experience designing and implementing robust data solutions to drive business insights and enhance decision-making. Expertise in AWS (S3, Lambda, Athena, EMR, Kinesis) and Azure (PaaS, IaaS, Blob, SQL Azure), including setting up data lakes, pipelines, and automating managed services. Hands-on with Spark, Hadoop (PySpark, Hive, Sqoop), real-time data integration using Kafka, HBase, and deploying Azure Multi-Factor Authentication (MFA). Proficient in Google Cloud Platform (GCP), Snowflake, and containerization with Docker. Skilled in building CI/CD pipelines (Jenkins), cloud SaaS deployments, and utilizing Airflow for data integration. Strong knowledge of front-end technologies (ReactJS, HTML, CSS) and experience migrating ETLs to cloud platforms. Adept in Agile and Waterfall SDLC methodologies, with a proven track record of architecting medium to large-scale BI solutions using cloud platforms like AWS, Azure, and GCP.

Country

Canada

City

Kitchener

Industry

Information Technology & Services

Skill

AWS API Gateway, Hadoop , SSIS , Data Marts, Tableau , CI/CD Pipelines, ETL/ELT Pipelines, Apache Airflow , AWS Glue, AWS Step Functions, Amazon Athena, Amazon Dynamodb, EC2, IAM, GCP Dataflow, GCP Cloud Storage, GCP Cloud Functions, GCP BigQuery, Data Lake Architecture, Data Warehouse Architecture

Experience

Senior Data Engineer

Sunnybrook

2024-9 - Present · 2 yrs 1 mo

Toronto, Ontario, Canada

Designed and implemented scalable ETL pipelines using Azure Data Factory and Databricks to support healthcare analytics and reporting. Developed and maintained PySpark-based data transformation logic for processing large volumes of clinical and operational data. Enabled end-to-end automation of data workflows by integrating Azure Data Lake Storage (ADLS) with Azure Synapse and SQL Database. Built reusable, parameterized ADF pipelines for efficient data ingestion from heterogeneous sources including FHIR APIs, flat files, and legacy systems. Enhanced data quality by implementing validation frameworks and automated testing within the data pipeline. Collaborated with data analysts and healthcare domain experts to translate analytical requirements into efficient data models and curated datasets.

Data Engineer

GE Vernova

2020-4 - 2023-12 · 3 yrs 9 mos

Bengaluru, Karnataka, India

Built scalable ETL pipelines using AWS Glue (PySpark), Lambda, and S3 for batch ingestion and transformation of industrial and IoT data. Designed and implemented data lakes on Amazon S3 with optimized storage policies (Standard, Glacier, Intelligent-Tiering) to reduce costs and improve retrieval performance. Leveraged AWS Redshift and Redshift Spectrum to enable analytical queries across structured and semi-structured data in S3. Migrated legacy on-premise ETL workflows and Oracle databases to Google Cloud Platform (GCP) using BigQuery, Cloud Composer (Airflow), and Dataflow. Developed orchestration DAGs in Airflow to schedule and monitor ETL processes across BigQuery, Cloud SQL, and Cloud Storage. Built and maintained PySpark-based data transformation scripts in Databricks, leveraging ML capabilities such as Mosaic AI and Genie for intelligent data enrichment. Integrated Snowflake for additional data warehousing and query acceleration, including automated ingestion with SnowPipe.

Data Engineer

UST

2018-6 - 2020-3 · 1 yr 10 mos

Bengaluru, Karnataka, India

As an AWS Data Engineer at UST Global, I was responsible for designing and implementing scalable, cloud-native data solutions to support enterprise analytics and reporting. My work focused on building robust ETL pipelines and automating workflows across a range of AWS services.Developed serverless data processing solutions using AWS Lambda, API Gateway, and DynamoDB to enable real-time data ingestion and processing. Built and optimized ETL pipelines using AWS Glue (PySpark) and orchestrated them with AWS Step Functions and Data Pipeline. Designed data lakes on Amazon S3 with appropriate partitioning and storage tier strategies to reduce cost and enhance performance. Utilized Redshift and Athena for advanced analytics on large-scale structured and semi-structured datasets.