Data Analyst
Resume:
WORK EXPERIENCE
John Deere
Moline, Illinois, USA
Role: Senior Data Analyst July 2024 - Present
Description: John Deere is a leading manufacturing company specializing in agricultural, construction, and forestry equipment, delivering innovative and high-quality machinery worldwide. I am analyzing production data, supply chain metrics, and sales trends using tools like SQL, Python, and Power BI to optimize manufacturing processes and forecast demand. I developed and maintained ETL pipelines, conducted statistical modeling, and applied predictive analytics.
Responsibilities:
Developed interactive dashboards and reports using Tableau, Power BI, and Looker to track key manufacturing metrics such as production efficiency, defect rates, and supply chain performance. These visualizations helped stakeholders.
Conducted Power BI training sessions and created user guides to support business users and improve adoption.
Wrote complex SQL queries to extract, transform, and analyze large datasets from manufacturing databases, ensuring accurate reporting and deep insights. Optimized queries for faster execution, enabling real-time analysis of production.
Utilized Python and R with libraries like pandas, NumPy, Matplotlib, seaborn, and scipy to perform statistical analysis, data visualization, and anomaly detection. These insights helped in identifying production inefficiencies.
Leveraged Excel (Advanced Level) for data cleaning, automation (macros, VBA), and scenario modeling, ensuring efficient handling of large datasets. Built automated reports and dashboards that provided instant insights into manufacturing
Designed ETL pipelines using tools like Informatica, Alteryx, and custom ETL scripts, integrating data from IoT sensors, production logs, and ERP systems. This ensured a centralized and structured data repository for real-time manufacturing.
Implemented big data solutions using Hadoop, Apache Spark, and Kafka, enabling real-time processing of high-volume manufacturing data. This allowed predictive analytics for maintenance scheduling and early detection of potential failures.
Worked with Azure Data Lake (Azure Data Lake, Azure Synapse Analytics) to store, manage, and analyze large-scale manufacturing datasets. These cloud solutions provided scalable, high-performance environments.
Built machine learning models using Python (scikit-learn, TensorFlow, PyTorch) for predictive maintenance, defect detection, and demand forecasting. These models reduced downtime, improved product quality, and optimized resource.
Applied statistical modeling and hypothesis testing using scipy and NumPy to assess production line variations, process deviations, and quality control improvements. This helped in reducing defects
Developed customer segmentation models to target high-value users using behavioral and demographic data
Optimized data warehousing solutions using Snowflake and Redshift, ensuring efficient query performance for real-time analytics and multi-source data integration. This allowed for seamless reporting across various departments.
Automated data validation and anomaly detection in production using Power BI, Tableau, and Python.
Designed automated workflows in HubSpot and Salesforce Marketing Cloud to improve lead nurturing and campaign targeting
Mapped and analyzed customer lifecycle data to improve retention, upsell, and cross-sell strategies.
Developed real-time streaming analytics using Kafka and Apache Spark, allowing continuous monitoring of manufacturing KPIs such as machine downtime and production bottlenecks. This ensured quick response.
Worked with Google Analytics and Google Tag Manager to track website traffic, user behavior, and conversion metrics
Collaborated with engineering and operations teams to translate business requirements into data-driven insights, improving production efficiency and cost management. This facilitated data-informed decision-making.
Automated workflow notifications and report refreshes using Power Automate to streamline report delivery.
Implemented forecasting and optimization models for inventory management and raw material procurement, reducing excess stock and supply chain inefficiencies. These models helped prevent overstocking.
Extracted data from REST APIs and ingested it into BI pipelines using Python.
Applied classification techniques to segment users based on purchase patterns and engagement behaviors.
Obtained Power BI, validating expertise in developing interactive dashboards, advanced analytics, and data-driven decision-making. This certification demonstrated proficiency in leveraging BI tools to enhance manufacturing
Environment: Advanced Excel, SQL, Python/R, Pandas, NumPy, Matplotlib, Seaborn, Scipy, Power BI, Tableau, Looker, Hadoop, Apache Spark, Kafka, Azure Data Lake, Azure Synapse Analytics, Snowflake, Informatica, Alteryx.
Baxter International
Deerfield, Illinois, USA
Role: Data Analyst Aug 2023 – May 2024
Description: Baxter International is a global healthcare company specializing in medical devices, pharmaceuticals, and biotechnology to advance patient care. As a Senior Data Analyst, I leveraged advanced analytics, ETL processes, and SQL-based querying to optimize healthcare data workflows, drive insights, and support decision-making. I utilized data visualization tools like Power BI and applied a deep understanding of healthcare data standards to support evidence-based solutions and improve patient outcomes.
Responsibilities:
Conducted in-depth data analysis to provide actionable insights that drove decision-making & improved patient outcomes, utilizing tools Excel (Advanced), SQL queries, & Python/R libraries (pandas, NumPy, Matplotlib, seaborn, scipy).
Developed and delivered interactive, insightful dashboards and reports using Tableau, Power BI, and Looker to present complex healthcare data in an understandable format for various stakeholders.
Implemented and managed ETL processes using tools like Informatica, Alteryx, or custom ETL pipelines built with Python or SQL to extract, transform, and load data across multiple systems and databases.
Applied machine learning algorithms and techniques using scikit-learn, TensorFlow, or Keras in Python to build predictive models for patient care outcomes, risk analysis, and operational efficiency.
Leveraged AWS cloud platforms such as S3, Redshift, Lambda, Glue, and Athena to design scalable data architectures, optimize storage, and ensure seamless data integration across systems.
Managed large-scale datasets and streamlined data processing using tools like Hadoop, Apache Spark, and Kafka to support complex healthcare analytics and real-time data processing.
Implemented A/B testing strategies and analyzed test performance to optimize campaign effectiveness and messaging
Worked with cross-functional teams, including data scientists, engineers, and healthcare professionals, using collaboration tools like Jira and Confluence to align data-driven initiatives with organizational goals.
Used advanced statistical techniques and tools like RStudio, Python (SciPy, Statsmodels), and Tableau to analyze healthcare data trends, uncover patterns, and inform data-driven strategies for improving healthcare delivery.
Ensured data quality and governance through rigorous validation, cleaning, and documentation procedures using Alteryx, SQL, and Python to meet healthcare standards and compliance requirements.
Presented findings and recommendations to senior leadership using Power BI, Tableau, and Looker, utilizing data visualization and storytelling techniques to guide strategic initiatives and healthcare policies.
Monitored and optimized data pipeline performance using tools like AWS CloudWatch, Apache Airflow, and Informatica to ensure efficient data processing and minimize delays in high-volume healthcare datasets.
Leveraged time series forecasting to predict campaign performance and optimize resource allocation.
Collaborated with IT teams to ensure data security and regulatory compliance using tools such as AWS Identity and Access Management (IAM), AWS KMS, and Redshift to protect sensitive healthcare data and maintain HIPAA compliance.
Conducted ad-hoc analysis for various departments, providing customized reports and actionable insights using SQL, Python, and Tableau to support business and clinical operations, ensuring improved patient care and operational effectiveness.
Environment: Excel (Advanced), SQL queries, Python (pandas, NumPy, Matplotlib, seaborn, scipy), Tableau, Power BI, Looker, Informatica, Alteryx, AWS S3, AWS Redshift, AWS Lambda, AWS Glue, Apache Spark, Kafka, Snowflake.
Lloyds Banking Group
Mumbai, India
Role: Data Analyst Oct 2021 – Nov 2022
Description: Lloyds Banking Group is a leading UK-based financial institution providing banking, insurance, and wealth management services to individuals and businesses. As a Data Analyst, I analyzed large datasets using SQL, Python, and Excel to drive business insights and enhance decision-making. I leveraged tools like Tableau and Power BI to create interactive dashboards and reports, supporting strategic initiatives in customer engagement and investment strategies.
Responsibilities:
Collected, cleaned, and pre-processed large datasets from multiple sources to ensure data quality and integrity using Excel (Advanced), enabling accurate reporting and decision-making for financial and operational analysis.
Analyzed financial and operational data to identify trends, patterns, and opportunities for cost savings or efficiency improvements, leveraging Python (pandas, NumPy) to perform advanced data manipulations and statistical analysis.
Built and maintained SQL queries for data extraction, transformation, and loading (ETL) processes, ensuring seamless integration of structured and unstructured data from various internal and external databases for better accessibility.
Developed and generated regular and ad-hoc reports to track key performance indicators (KPIs) related to banking operations, customer behaviour, and financial performance using Excel, SQL, and Tableau.
Created data visualizations and interactive dashboards using Tableau, Power BI, and Looker to transform complex datasets into meaningful insights, enabling business stakeholders to make data-driven decisions efficiently.
Supported fraud detection and risk management efforts by analyzing transaction data and identifying anomalies or suspicious activities with SQL, Python, and Apache Spark.
Collaborated with cross-functional teams, including marketing, risk, and compliance, to provide actionable insights for business growth and regulatory adherence using Excel and Tableau.
Applied machine learning algorithms and statistical methods in Python (scikit-learn, TensorFlow) to develop models for credit scoring, loan default prediction, and customer segmentation.
Performed data validation and data integrity checks to ensure compliance with regulatory standards and banking policies, such as those related to privacy and security using SQL, Python, and ETL tools like Informatica.
Analyzed customer behaviour, including transaction history, to improve customer experience and recommend targeted products and services with SQL, Python, Tableau, and Power BI.
Continuously monitored and optimized data pipelines and database performance to ensure timely access to high-quality data for analysis and decision-making using SQL, AWS S3, AWS Redshift, and ETL tools.
Environment: Excel (Advanced), SQL, Python (pandas, NumPy), Tableau, Power BI, Looker, scikit-learn, TensorFlow, Apache Spark, Informatica, AWS S3, AWS Redshift.
Marks & Spencer
Mumbai, India
Role: Programmer Analyst Jun 2020 – Sep 2021
Description: Marks & Spencer is a leading British multinational retailer specializing in clothing, food, and home products, known for its quality, sustainability, and innovation. As a Programmer Analyst, I worked with Java, SQL, and Spring Boot to develop and maintain web applications, ensuring seamless integration with RESTful APIs and databases.
Responsibilities:
Developed and implemented predictive models for user behaviour data on websites, including URL categorization, social network analysis, and search content, using machine learning techniques.
Wrote Python scripts with Apache Spark and Elastic Search to create real-time dashboards visualized in Grafana.
Led the development of Natural Language Processing (NLP) initiatives, including chatbots and virtual assistants.
Defined business questions and created databases based on schema of key features and metrics using SQL in AWS RDS.
Designed and implemented ETL pipelines for data extraction, transformation, and loading from multiple data sources into AWS RDS and S3, ensuring data consistency and availability for analysis.
Managed data processing using Hadoop, HDFS, MapReduce, Hive, Pig, and Spark in AWS EMR, enabling scalable distributed computing for large datasets. Optimized data workflows to enhance efficiency and reduce processing time.
Published Power BI reports and created interactive dashboards accessible via web clients and mobile apps, allowing real-time data-driven decision-making. Designed intuitive visualizations to help stakeholders analyze trends effectively.
Used Power BI Gateways to establish a secure and seamless connection between on-premises data sources and cloud services, ensuring that dashboards and reports remained updated. Enabled automated data refresh.
Automated data validation and cleansing processes using Python and Excel macros, significantly reducing manual data preparation efforts. Enhanced data accuracy and consistency by implementing rule-based validation techniques.
Automated routine data processing tasks using Python and AWS Lambda, optimizing workflow efficiency.
Stored and retrieved large datasets in AWS S3 for the company’s personal marketing website.
Developed data analysis prototypes using Power BI & Power Pivot, visualized reports with Power View & Power Map.
Implemented partitioning and bucketing techniques in Hive to improve query performance.
Explored and visualized data using Power BI and Tableau, providing insights across various dimensions.
Installed and configured Enterprise Gateway and Personal Gateway in Power BI Services for seamless data integration.
Environment: Python, Apache Spark, Elastic Search, Grafana, Natural Language Processing (NLP), chatbots, virtual assistants, SQL, AWS RDS, Hadoop, HDFS, MapReduce, Hive, Pig, AWS EMR, AWS S3, AWS EC2, Power BI, Power Pivot, Power View, Power Map, Power BI Gateways, Tableau, Google DialogFlow, Enterprise Gateway, Python, AWS Lambda,
Jivithesh Reddy Tamma
Data Analyst
Motivated and detail-oriented Data Analyst Around 5+ years of experience, a proven track record of utilizing data-driven insights to drive strategic decisions. Proficient in data visualization, statistical analysis, and ETL processes, with expertise in SQL, Python, and BI tools such as Power BI and Tableau.
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PROFILE SUMMARY
Proficient in Advanced Excel functionalities, including pivot tables, macros, VBA scripting, and data analysis tools.
Experienced in Python and R for data manipulation, statistical analysis, and visualization, utilizing libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scipy.
Hands-on experience with AWS (S3, Redshift), Google BigQuery, and Azure Data Lake for cloud-based data storage and analytics.
Proficient in data analysis, data visualization, and delivering actionable insights to support business decision-making.
Skilled in writing complex SQL queries, optimizing database performance, and working with relational databases like MySQL, PostgreSQL, and Oracle.
ML models using Python to identify trends and enhance predictive analytics in Power BI.
Familiar with Hadoop ecosystems, Apache Spark, and Kafka for handling large-scale data processing and streaming.
Competent in developing insightful dashboards using Tableau, Power BI, and Looker to present actionable business insights.
Skilled in using ETL tools like Informatica, Alteryx, and designing custom ETL pipelines for efficient data transformation and integration.
Proficient in applying machine learning algorithms using Python (Scikit-learn, TensorFlow) for predictive analytics, regression, classification, and clustering.
Experienced in time series analysis, forecasting, and analyzing trends using advanced machine learning algorithms.
Knowledgeable in training neural networks for classification, image recognition, and other AI-driven analytics.
Experienced in using Snowflake for data warehousing, querying large datasets, and optimizing analytical workflows.
Strong background in hypothesis testing, ANOVA, and other statistical methods for data interpretation. Conducts A/B testing to drive data-driven decisions for business strategies.
Familiar with data privacy and security standards, ensuring compliance in data handling processes.
Experienced in Agile methodologies, including Scrum and Kanban, for project execution. Automates recurring reports using Python scripting, reducing manual effort and improving efficiency.
EDUCATION
Master’s in Computer Science, University of Illinois, Illinois, USA.
TECHNICAL SKILLS
Data Manipulation and Analysis
Advanced Excel
SQL (MySQL, PostgreSQL, Oracle)
Python
R
Data Visualization
Tableau, Power BI (Power BI Certified), Looker
Big Data and Cloud Technologies
Hadoop, Apache Spark, Kafka
AWS (S3, Redshift), Google BigQuery, Azure Data Lake
Snowflake (Data warehousing and analytical workflows)
ETL and Data Integration
ETL Tools (Informatica, Alteryx, custom ETL pipelines)
Machine Learning and AI
Machine Learning (Scikit-learn, TensorFlow, Keras, regression, clustering, predictive modeling)
Time Series Analysis and Forecasting
Natural Language Processing
Deep Learning
Statistical Analysis and Testing
Statistical Analysis
A/B Testing and Experimentation
Collaboration and Agile Practices
Agile Frameworks (Scrum, Kanban)
Data Governance and Automation
Data Privacy and Security Standards
Report Automation
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