Senior Data Engineer and Analytics Engineer with 7+ years of experience building scalable cloud architectures, optimizing ETL pipelines, and driving data analytics for decision-making. Proficient in AWS, Azure, Databricks, Snowflake, DBT and Google Cloud (GCP) with expertise in Python, SQL, and PySpark. Skilled in implementing end-to-end CI/CD pipelines using Infrastructure as Code (IaC) practices with tools like CloudFormation, Terraform, Jenkins, and CodePipeline to streamline automation and deployment.My expertise includes developing ETL pipelines using AWS Glue, Lambda, S3, Redshift, Athena, and CloudFormation, ensuring robust data governance and security compliance. I’ve also worked with Azure Data Factory, Azure Synapse, and Azure Databricks to automate data flows and deploy real-time analytics dashboards in Power BI and Tableau, covering areas like finance, supply chain, and sales analytics.On Google Cloud Platform (GCP), I’ve integrated BigQuery, Dataflow, and Terraform for scalable data warehousing and real-time processing, ensuring secure, efficient data pipelines. I’ve developed data visualizations using AWS Quicksight, Tableau, and Power BI, and implemented data validation, performance monitoring, and root cause analysis using AWS CloudWatch and Databricks monitoring. In collaboration with product teams and data scientists, I’ve delivered high-impact solutions, improving reporting accuracy by 35% and reducing costs by 40% for IoT data processing. With proficiency in agile methodologies, Git, CI/CD tools like CodePipeline and Jenkins, and automation, I consistently drive data-driven decision-making and scalable cloud-based solutions.I'm eager to connect for data engineering, cloud architecture, and analytics roles. Let’s build innovative data-driven systems together!SOFTWARE SKILLS Programming Languages: Python, Java, SQL, Scala, PySpark, Shell Scripting, C, C++, JavaScriptCloud Platforms: AWS (S3, Glue, Redshift, Lambda, EC2, RDS, CloudFormation, SAM, DynamoDB), MS Azure (Azure Data Factory, Azure Databricks, Azure Blob Storage, Azure Data Lake, Azure Functions, Event Hub), Databricks. GCP, Snowflake, DBTDatabases: SQL Server, PostgreSQL, MongoDB, NoSQL, Apache HBase, Druid, CassandraData Tools: Apache Spark, Kafka, Hadoop, SSIS, SSRS, SSAS, Airflow, Apache NiFi, Apache Sqoop, Informatica, QlikAnalytics & Visualization: Tableau, Power BI, AWS Athena, Quicksight, Cognos, Grafana, Alteryx, DenodoCI/CD & DevOps: AWS CodePipeline, GitHub Actions, YAML, Kubernetes, Docker, Terraform
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Data Engineer 2AmazonSeattle, Wa, Us
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Senior Data EngineerAgco Corporation Jun 2022 - PresentDuluth, Georgia, Us• Led the harmonization of ERP data to AWS DataHub for AGCO's supply chain using AWS services (Redshift, Glue, CloudFormation, S3, Step Functions), integrating data from SAP, Oracle JDE, and Salesforce. This effort resulted in a 50% improvement in data accessibility, a 40% reduction in processing time, and 30% faster reporting, enhancing decision-making efficiency and reducing operational costs by 20%. Developed financial dashboards using Informatica and Snowflake, achieving a 20% increase in reporting efficiency. Used Snowflake's Virtual Warehouses, Streams, SQL and DBT for transformations, with Git for version control, and visualized results in Power BI• Implemented data ingestion and warehousing using Azure Data Factory, Azure Data Lake, Hive, and Spark, boosting processing speed by 40% with advanced SQL techniques. Improved data quality and governance by developing health scorecards and monitoring systems.• Leveraged Databricks for real-time IoT sensor data transformations with Delta Lake, Apache Spark, Kafka, and Python, delivering a 40% improvement in data processing speed and a 30% increase in equipment performance insights, along with a 50% reduction in data retrieval time and 20% operational cost reduction through the automation of 125+ data pipelines.• Led data integration and warehousing on GCP for AGCO Corporation, centralizing data from ERP systems (SAP, Oracle JD Edwards, Salesforce) using Google Cloud Storage, Dataflow, BigQuery, and Cloud Composer. Enhanced data processing efficiency by 30% with Dataproc and increased processing speed by 40% through tuning Dataflow and BigQuery. Ensured data security and compliance with GDPR and HIPAA using IAM, encryption, and VPC Service Controls. Established data governance with Google Cloud Data Catalog and enforced data quality in BigQuery. Integrated machine learning models with TensorFlow, BigQuery ML, and AI Platform, improving supply chain metrics and operational efficiency. -
Data Science | Data Engineering | Cloud Architecture | Business IntelligenceAgco Corporation Jun 2021 - Jun 2022Duluth, Georgia, Us• Developed financial, Marketing dashboards using Informatica and Azure Data Lake, achieving a 20% increase in reporting efficiency. Conducted statistical testing on large datasets, ensuring data quality and integrity.• Worked on AWS EMR, Spark, HDFS, and Hive to process and analyze large-scale finance data, optimizing data pipelines and enabling faster data aggregation and reporting. This solution improved query performance by 30%, delivering actionable insights to support financial decision-making• Financial Intelligence: Revolutionized the Grain & Protein Team’s financial dashboard by leveraging Tableau, Python, SQL, and AWS, achieving a 20% efficiency increase. This innovation translated into a 10% improvement in monthly financial reporting accuracy, setting a new benchmark in digital financial data management.• Led the migration from AWS Redshift to Databricks, leveraging Delta Lake's ACID transactions and CI/CD pipelines with GitHub Actions, Databricks CLI, and Asset Bundles. This transition reduced deployment times by 50% and cut infrastructure costs by 30%, enhancing scalability and data processing• Data Transformation Expertise: Conducted innovative data blending and preprocessing using AWS Lambda, Glue, Athena, and Cloudwatch. This expertise led to a 22% improvement in data consumption efficiency, enabling a 15% faster delivery of insights to key stakeholders and setting new industry standards in data utilization.
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Research AssistantUniversity Of Illinois Urbana-Champaign Apr 2021 - Jun 2021Champaign, Il, Us• Research Assistant: Assisted Professor Kim in research work related to Web scraping, Software automation testing, Natural Language Processing and Data visualization. Activities involved scraping static and dynamic data from webpage using Python, BeautifulSoup, Automation testing using Selenium Web driver, PyTest, Sentimental Analysis using NLTK performing visualization using MS PowerBI and Python Plotly-Dash, deployment on Azure Cloud and database management and Query Analysis using MySQL. -
Data EngineerBarclays Apr 2020 - Dec 2020London, Gb• Developed and deployed scalable Python applications for ETL processes using Azure Data Factory, Azure Functions, Azure Databricks and Apache Spark. Maintained version control with Git and performed extensive control testing for data accuracy.• Utilized Kubernetes for containerization and orchestration, improving application reliability and performance.• Built data warehouse solutions in SQL Server and Snowflake, using SSIS for data extraction and SnowSQL for data loading into Snowflake. Utilized DBT for transformations, SQL and YAML for configurations, Git for version control, and Tableau for data visualization.• Developed and deployed a real-time ETL dashboard on AWS using Elastic Beanstalk, EC2, and S3 to host a Python Flask app with Bootstrap, JavaScript, Plotly, and Dash for dynamic data visualization.Led the implementation of a CI/CD pipeline using AWS CodePipeline, Git, and Azure DevOps, automating code deployments, reducing deployment time by 50%, and enhancing operational efficiency for data engineering and cloud workflows. -
Python Developer Teaching AssistantCoding Ninjas Jan 2020 - Apr 2020New Delhi, Delhi, In -
Data EngineerJohnson & Johnson Apr 2019 - Dec 2019New Brunswick, Nj, UsLTI Client Developed Comprehensive Azure-Based Data Solutions: Enhanced Johnson & Johnson’s data integration and analysis by implementing Azure Blob Storage, Azure Data Factory, and Snowflake on Azure to centralize and streamline data management. Engineered Advanced Data Pipelines and Automation: Utilized SQL, Python, and Unix Shell scripting to create and automate data pipelines, ensuring accurate data aggregation, cleansing, and efficient retrieval processes.Integrated Machine Learning Models: Deployed Random Forest, K-Means Clustering, and Neural Networks using Azure Machine Learning Service to improve predictive analytics, risk assessment, and pattern recognition across large datasets.Optimized CI/CD Processes with Azure DevOps: Configured continuous integration and deployment pipelines, enabling reliable and seamless updates to data processing workflows, enhancing operational efficiency.Achieved Significant Data Accuracy and Efficiency Gains: Improved data accuracy by 35% and increased data retrieval efficiency by 40%, empowering better decision-making and advanced analytics for business units at Johnson & Johnson. -
Senior Software EngineerLtimindtree Jun 2017 - Dec 2019Mumbai, Mh, In• Architected and deployed secure financial data solutions for ABSA Bank using AWS services (S3 withserver-side encryption, KMS, IAM), implementing CI/CD with AWS CodePipeline, CodeCommit, andCloudFormation. Reduced deployment time by 50% and enhanced cloud security compliance by 35%,ensuring data privacy and regulatory compliance.• Optimized Alteryx workflows for financial reporting by integrating MySQL and developing Power BIdashboards, while also designing cloud-based data solutions using AWS (S3, Glue, Athena, Redshift) andPySpark. Achieved a 40% increase in reporting accuracy, 35% faster decision-making, and improvedoperational efficiency by 35%, contributing to a 20% increase in business growth.• Led the automation of DevOps processes by integrating GitHub Actions and AWS CodeBuild into theCI/CD pipeline, streamlining deployment workflows and improving system uptime. Achieved a 60%reduction in manual intervention, enhanced deployment reliability, and decreased error rates by 25%,significantly boosting operational efficiency and team productivity.• Implemented data solutions with Azure Blob Storage, Azure Data Factory, Snowflake, and DBT, contributing to a 35% improvement in data accuracy. Used SQL and Bash scripting on Linux for automation, resulting in a 40% increase in efficiency. -
Software Developer, Johannesburg & Cape TownAbsa Group Sep 2018 - Apr 2019Johannesburg, Johannesburg, ZaLTI Client Developed backend frameworks using Python, Django, Flask along with integrating databases like MongoDB, PostgreSQL, Data Storage Amazon S3. Analyzed data by interfacing the software with partner system, performing data modeling as well as performed visualization and analysis using D3.js, Tableau, SQL queries, R Shiny and hosted web-based dashboard on AWS and Azure cloud for decision making. Collaborated with stakeholders, worked on cloud-based hosting solutions like AWS S3, ML Azure, developed hypothesis, collected and transformed data to provide optimal solutions to optimize several insurance Product -
Data AnalystLarsen & Toubro Jun 2015 - Oct 2015Mumbai, Maharashtra, In
Kartik Jadhav Education Details
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University Of Illinois Urbana-ChampaignInformation Management ( Data Science And Analytics ) -
Vivekanand Education Society'S Institute Of TechnologyComputer Science
Frequently Asked Questions about Kartik Jadhav
What company does Kartik Jadhav work for?
Kartik Jadhav works for Amazon
What is Kartik Jadhav's role at the current company?
Kartik Jadhav's current role is Data Engineer 2.
What schools did Kartik Jadhav attend?
Kartik Jadhav attended University Of Illinois Urbana-Champaign, Vivekanand Education Society's Institute Of Technology.
Who are Kartik Jadhav's colleagues?
Kartik Jadhav's colleagues are Karolina Wieczorek, Jessica Chromy, Abdul M., Jennifer Z, Sofia Arredondo, Junjie Lu, Nita Ashas.
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