5+ years of extensive experience in Analyzing, Designing and Implementing Data Analysis and Engineering strategies for both small and large-scale data environments. Experience in all stages of the Data Lifecycle, includi…
5+ years of extensive experience in Analyzing, Designing and Implementing Data Analysis and Engineering strategies for both small and large-scale data environments. Experience in all stages of the Data Lifecycle, including Data Collection, Data Cleaning, Data Analysis, Data Visualization, and Data Reporting. Experience as a Data Analyst and Engineer focused on data integrity, robustness, performance, and creating designs for data-driven decision-making. Experience in both Waterfall and Agile project environments. Experienced in Data Analysis and Engineering using tools like Python, R, SQL, Tableau, and Hadoop. Expertise in handling large datasets across multiple platforms such as AWS, Azure, and Google Cloud. Built several data pipelines and ETL processes from scratch using Python, SQL, and various data engineering frameworks like Apache Spark and Kafka. Good experience in Data Visualization using Tableau, Power BI, and matplotlib to create insightful and actionable reports. Experience in Designing & Implementing Data Models, including relational and non-relational databases using PostgreSQL, MySQL, MongoDB, and Cassandra. Worked with Jenkins under the CI/CD pipeline, continuously improving Agile Data Development. Experience in building data integration tools like Talend and Informatica. Extensively used complex SQL queries daily using JOINS and Nested SQL queries for backend database management and reporting using SQL Studio. Performed Data Quality and Integrity testing for data warehouses and large-scale data environments. Hands-on experience with Big Data technologies, including Hadoop, Spark, Hive, and Pig. Extensively worked on data security and privacy, ensuring compliance with standards such as GDPR and HIPAA. Hands-on experience in Systems Analysis, Data Integration, UAT, positive and negative, functionality testing, and regression testing. Prepared cycles for executing manual data validation tests, mapping them with requirement traceability matrices, and providing test metrics for data project closure. Experienced in performance tuning and optimization of SQL queries and data processing tasks. Experience in mobile data analytics using tools like Firebase and Mixpanel, which are used to analyze mobile application data on Android and iOS. Proficient in managing databases of applications developed with SQL Server, Oracle, DB2, and MS Access. Strong in writing SQL queries to perform backend data validations. Excellent experience with source version control tools like Subversion (SVN), Team Foundation Server (TFS), and GIT. Skilled at conducting various data analysis methods, viz. Descriptive, Diagnostic, Predictive, and Prescriptive Analytics. I am proficient in Data Strategy Preparation, Data Environment Setup, Data Pipeline Preparation, Data Planning, Data Case Management, and Data Tracking. Proficient in finding and handling data elements using various querying and data manipulation techniques to maintain data in a stable condition. Extensive experience in relational Data modeling, Dimensional data modeling, logical/Physical Design, ER Diagrams and OLTP and OLAP System Study and Analysis.
• Architected and implemented scalable data pipelines using Python and Apache Airflow for ETL processes handling terabytes of data daily.
• Engineered data models and optimized database performance on PostgreSQL and Redshift for efficient query execution and storage management.
• Developed real-time monitoring dashboards in Tableau to visualize KPIs and business metrics, facilitating data-driven decisions.
• Automated data ingestion from AWS S3, API sources, and IoT devices using Lambda functions and Kinesis streams, reducing data latency by 30%.
• Implemented data validation rules and quality checks using SQL and Python scripts to ensure data integrity and compliance with GDPR regulations.
• Integrated machine learning models into data pipelines for predictive analytics and recommendation systems, improving user engagement by 25%.
• Collaborated with data science team to deploy NLP models for sentiment analysis on customer feedback data, enhancing customer experience insights.
<p>Architected and implemented scalable data pipelines using Python and Apache Airflow for ETL processes handling terabytes of data daily.<br>
Engineered data models and optimized database performance on PostgreSQL and Redshift for efficient query execution and storage management.<br>
Developed real-time monitoring dashboards in Tableau to visualize KPIs and business metrics, facilitating data-driven decisions.<br>
Automated data ingestion from AWS S3, API sources, and IoT devices using Lambda functions and Kinesis streams, reducing data latency by 30%.<br>
Implemented data validation rules and quality checks using SQL and Python scripts to ensure data integrity and compliance with GDPR regulations.<br>
Integrated machine learning models into data pipelines for predictive analytics and recommendation systems, improving user engagement by 25%.<br>
Collaborated with data science team to deploy NLP models for sentiment analysis on customer feedback data, enhancing customer experience insights.</p>
<p>Conducted advanced statistical analysis and machine learning modeling using Python (Pandas, scikit-learn) to derive insights and forecast trends in customer behavior.<br>
Designed and optimized SQL queries for complex data extraction and aggregation from large-scale relational databases (Oracle, MySQL).<br>
Developed automated reporting systems using Tableau, Google Data Studio, and Power BI, reducing report generation time by 40%.<br>
Led A/B testing initiatives to optimize marketing campaigns and product features, resulting in a 15% increase in conversion rates.<br>
Collaborated with cross-functional teams to implement data-driven strategies and segmentation models for personalized customer targeting.<br>
Analyzed web traffic and user interaction data using Google Analytics to optimize SEO strategies and content performance.<br>
Created data visualization dashboards in Tableau to track key performance indicators (KPIs) and business metrics, presenting insights to stakeholders.<br>
Identified data requirements and created data marts to facilitate analytical reporting across departments.</p>
<p>Extracted data from AWS Redshift using PostgreSQL to generate company-wide Tableau reports and dashboards.<br>
Performed data cleaning, manipulation, and sampling on large multi-gigabyte datasets using Python and SQL.<br>
Implemented K-Mode clustering analysis with Python on Linux to segment website visitors based on engagement patterns and time spent in specific categories.<br>
Collaborated with internal stakeholders to drive strategic initiatives and provide actionable insights for business decisions.<br>
Utilized advanced Excel functions to support the revenue team with ad-hoc analysis for affiliate marketing strategies.<br>
Analyzed social media data from platforms like Facebook, Instagram, and Twitter to measure post-performance using metrics such as impressions, clicks, and CPC.<br>
Conducted hypothesis tests to analyze the impact of new website features like CTA buttons and banners on performance metrics such as CTR and RPM.<br>
Configured critical reports in Google Analytics using Attribution Modeling to identify conversion points and optimize marketing efforts.<br>
Analyzed survey data to calculate Net Promoter Score (NPS) for measuring brand loyalty among website users.</p>
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