Satya K. Email & Phone Number

Sterling, Va, Us

Montgomery, Alabama, United States

As a Senior Data Engineer with the State of Alabama since April 2022, I have led critical data migration initiatives, transferring petabyte-scale datasets from Teradata to Snowflake with an emphasis on accuracy and efficiency. Through custom Python-based ETL pipelines and Apache Airflow automation, I achieved a 99.5% data accuracy rate, significantly enhancing data quality and accessibility across departments. I implemented predictive models in Python and DataRobot to support accurate billing and revenue forecasting, providing stakeholders with reliable insights to guide decision-making.In this role, I introduced real-time data integration using Apache Kafka, enabling seamless access to customer data for cross-functional analytics teams. I also collaborated with business units to establish KPIs and crafted interactive Tableau dashboards, presenting insights on customer behavior, revenue, and platform performance. My work has underscored my ability to blend technical expertise with a collaborative approach, ensuring each project aligns with the State’s strategic goals while fostering cross-functional alignment and communication.

Los Angeles, Ca

Served as a Big Data Engineer for the Housing Department of California, where I developed a high-volume big data platform that enabled efficient distributed processing and seamless data integration. I designed scalable ETL pipelines using Apache Spark and PySpark to transform data from multiple sources, including Oracle, MySQL, and flat files, in a multi-node Hadoop environment. Leading the migration from SQL servers to Azure Data Lake Storage (ADLS) via Azure Data Factory, I optimized the department’s data infrastructure, reducing dependency on on-premises storage and improving scalability.To facilitate real-time data updates, I integrated Apache Kafka with Spark Streaming, ensuring consistent, timely data delivery for downstream analytics. I also built machine learning pipelines using PySpark and Spark MLlib, contributing predictive insights that supported key decision-making processes. Recognized for my impact on processing efficiency, I successfully reduced overall data processing time by 30%, streamlining reporting capabilities across various analytics teams and enabling more agile responses to department needs.

Chicago, Illinois, United States

As a Senior Data Engineer at Uptake Technologies, where I designed and implemented complex ETL pipelines across AWS Glue and Azure Data Factory. Processing over 8TB of data across multi-cloud environments, I streamlined data workflows and achieved a 20% reduction in processing time. Additionally, I led the setup of CI/CD pipelines using Jenkins, enhancing deployment efficiency and minimizing manual intervention, which improved release frequency and reduced errors.I collaborated closely with cross-functional teams to understand data requirements across analytics, finance, and product operations, ensuring seamless pipeline integration to meet diverse business needs. As a mentor to junior engineers, I provided guidance on Git best practices, version control, and code reviews, fostering a collaborative, knowledge-sharing environment. By incorporating Apache Kafka for real-time data processing, I enabled low-latency insights for executives, enhancing their ability to make data-driven decisions. Power BI dashboards I developed further empowered stakeholders, providing visual insights into data health and pipeline performance.

Columbus, Ohio Metropolitan Area

In my role as a Data Engineer at Nationwide from June 2016 to May 2018, I developed custom multi-threaded ingestion jobs using Java and Scala, as well as Spark JDBC jobs for ingestion from FTP servers and multiple data warehouses. I designed and optimized Spark applications in PySpark for efficient ETL processes, leveraging Spark SQL to enhance query performance and support downstream analytics. Leading data migrations from Oracle and MySQL databases to Azure Data Lake with Azure Data Factory and Python scripts, I streamlined data workflows, improving both scalability and operational reliability.I integrated Kafka with Spark Streaming to facilitate real-time data transformations, providing timely, consistent data availability for analytics teams. My responsibilities included orchestrating ETL workflows through Apache Airflow, enabling reliable data integration and fault tolerance across the pipelines. I also implemented Snowflake for data warehousing, optimizing data models and SQL queries for high-performance retrieval. This experience solidified my skills in managing large-scale data migrations and implementing real-time solutions that align with business goals.

Hyderabad, India

I architected and implemented BI solutions on Azure, utilizing Azure Data Lake, Data Factory, Azure SQL Data Warehouse, and Databricks to support complex healthcare data management and analytics. I led the migration of on-premises databases (SQL Server, Oracle, and DB2) to Azure Data Lake Storage (ADLS) using Azure Data Factory, reducing infrastructure dependencies and ensuring scalable data storage. By building ETL pipelines with Snowflake, I enabled real-time access and optimized query performance, providing efficient solutions for healthcare data analysis.Using dbt, I developed robust data models that standardized business logic, enabling self-service analytics across departments and improving data accessibility. My work in automating ETL processes with Azure Synapse and ADF enhanced data quality, consistency, and operational efficiency. I established CI/CD pipelines in Azure DevOps to streamline deployments and improve pipeline reliability. Additionally, I applied Azure AD-based access controls to ensure HIPAA compliance, fostering secure and governed data environments. This role honed my expertise in building adaptable and compliant data solutions in a highly regulated industry.