Senior Data Analyst

Snehith Chiluka

United States PH: 815-***-**** Email Id: **********@*****.***

LinkedIn https://www.linkedin.com/in/snehith-c-27a39a237/

Senior Data Analyst

PROFESSIONAL SUMMARY

Senior Data Analyst with Over 9 years of experience as a Senior Data Analyst, with a strong focus on data visualization using Tableau and Power BI to deliver actionable insights.

Expertise in managing and optimizing ETL processes to ensure efficient data extraction, transformation, and loading for large datasets across healthcare and IT industries.

Extensive experience working with BigQuery for large-scale data analysis, optimizing query performance and improving data processing speed.

Proven track record in healthcare data analysis, translating complex HR and operational data into insights that improved patient care and workforce efficiency.

Demonstrated ability to handle information technology operational data, improving system performance and customer satisfaction through data-driven insights.

Strong proficiency in SQL for writing and optimizing complex queries, enabling faster data retrieval and more accurate reporting.

Implemented data lineage strategies to trace the flow of data across systems, enhancing data governance and transparency.

Applied decision science techniques to identify patterns and trends in both healthcare staffing and IT operational data, leading to improved resource allocation and performance.

Successfully managed full lifecycle of data reporting projects, from gathering requirements to performing UAT, production deployment, and demo presentations.

Collaborated on Business Requirements Documents (BRD) to ensure alignment between business needs and data solutions

Collaborated with data engineers and IT professionals to develop and maintain scalable data pipelines and workflows for HR and operational reporting.

Automated data analysis and reporting tasks using SQL and BigQuery, reducing manual efforts and improving data accuracy by 30%.

Expertise in data modeling, improving both healthcare and IT reporting structures to ensure data integrity and consistency across all systems.

Strong background in collaborating with both technical and non-technical stakeholders, translating complex data insights into understandable and actionable recommendations.

Developed and maintained healthcare-specific HR metrics dashboards to track staffing efficiency, employee retention, and regulatory compliance.

Led the integration of large datasets for IT operational metrics, providing leadership with clear insights into system performance and customer service levels.

Proven ability to demo and present data solutions to leadership, ensuring stakeholder buy-in and successful adoption of new reporting tools.

Demonstrated ability to manage cross-functional teams and provide expert guidance on data analysis, data management, and data visualization best practices.

Applied statistical analysis and machine learning models to predict trends in workforce management and IT system performance.

Strong focus on maintaining data quality, identifying and resolving data discrepancies to ensure accurate and reliable reporting.

Successfully developed production-ready reports and dashboards that provided real-time access to critical metrics, improving decision-making capabilities.

Continuous pursuit of professional development in data analytics, staying updated with the latest advancements in cloud platforms, data science, and ETL technologies.

Skill Set:

Programming Languages SQL, Python, R, Java, Shell Scripting, DAX and IBCO Spotfire

Databases Oracle, SQL Server 2008/2012/2016/2019 Analysis Services Tabular, MySQL, Cassandra, Teradata, PostgreSQL, MS Access, Snowflake, NoSQL, IBM DB2, HBase, MongoDB, BigQuery (Google Cloud Platform)

Cloud Platforms Azure: App Service, SQL Database, Cosmos DB, Active Directory, Functions, Blob Storage, Table Storage, Redis Cache, Service Bus, Event Hub, Logic Apps, Kubernetes Service (AKS), DevOps, Application Insights, CDN, Key Vault.

AWS: Lambda, S3, EC2, SQS, SNS, DynamoDB, CodeDeploy, Elastic Beanstalk, and CloudFormation, Athena.

Data Visualization Tools Tableau, Power BI, Palantir Foundry, Databricks, Denoda,, Looker, SAS, and Excel

Analytical Tool SageMaker

Batch Processing Hive, MapReduce, Pig, Spark

Operating System Linux/Unix and Microsoft Windows

Reporting Tools Pentaho, SSIS, and SSRS.

CI/CD Apache Airflow, Databricks

Project Management Jira, Azure Devops, MS Visio, MS Office, SharePoint, Team Foundation Server, Microsoft One and GIT

ETL Halo BI, Amazon Data Pipeline, Alteryx

Healthcare Claims Management QNXT

Machine Learning TensorFlow, PyTorch, Scikit-learn, Regression, Classification, Clustering, Decision Trees, Random Forest, Neural Networks, SVM, Model Deployment (TensorFlow Serving, Flask, FastAPI)

PROFESSIONAL EXPERIENCE

Senior Data Analyst May2023 to present Date

Client: Sentara Healthcare Norfolk,VA Responsibilities:

Utilized Tableau and Power BI to design real-time dashboards that provided actionable insights on HR metrics, employee performance, and healthcare workforce management.

Translated complex healthcare HR data into clear insights for leadership, enabling data-driven decisions to improve staffing efficiency and patient care.

Applied BigQuery to handle large datasets related to HR and healthcare operations, enhancing the scalability and efficiency of reporting systems.

Led the end-to-end process of gathering requirements, conducting UAT, and presenting healthcare-related HR reporting solutions to stakeholders.

Leveraged Collibra's data quality features to set up quality metrics, monitor data accuracy, and resolve discrepancies, enhancing the reliability of datasets used in reporting and analysis.

Used decision science techniques to analyze HR trends in healthcare staffing, improving workforce planning by predicting hiring needs based on patient care demands.

Assisted in ensuring compliance with data privacy regulations by using Collibra to monitor sensitive data usage and manage access controls effectively.

Developed ETL pipelines for integrating HR and healthcare employee data from multiple sources, ensuring accuracy and real-time reporting.

Collaborated with cross-functional teams to translate healthcare operational metrics into HR-focused dashboards, improving staff allocation and scheduling efficiency.

Used Dayforce to extract and analyze employee data for HR metrics reporting, enabling insights into workforce trends, retention rates, and performance metrics

Developed Power BI dashboards for visualizing key metrics, empowering stakeholders with real-time insights and data-driven decisions.

Led demos for HR and healthcare administration, ensuring they could leverage new tools for better employee management and regulatory compliance.

Conducted data validation and UAT for healthcare HR reports, ensuring alignment with patient care standards and internal workforce metrics.

Worked closely with healthcare leadership to set goals for HR metrics, focusing on improving employee retention, satisfaction, and performance within the healthcare context.

Applied decision science methodologies to uncover patterns in healthcare staffing, enabling better resource allocation and scheduling based on patient load forecasts.

Automated healthcare workforce reports to monitor key metrics such as nurse-to-patient ratios and on-call staff efficiency.

Collaborated with healthcare teams to gather and define reporting requirements, ensuring HR data solutions aligned with patient care quality standards.

Improved healthcare workforce productivity by developing HR reports that optimized staff distribution based on historical patient care data.

Led ETL processes to ensure healthcare employee data, including certifications and compliance records, were accurately maintained and reported.

Applied SQL and BigQuery to enhance healthcare staffing reports, ensuring data integrity and real-time access to workforce metrics.

Presented healthcare-related HR data insights to both technical and non-technical stakeholders, improving decision-making for patient care and employee management.

Led cross-functional projects that used HR data analytics to improve the efficiency of healthcare operations, particularly in staff scheduling and patient care coverage.

Developed production-ready healthcare HR dashboards to track metrics on staff efficiency, certifications, and patient care outcomes, improving leadership’s ability to make informed decisions.

Senior Data Analyst July 2022 to May 2023

Client: TD Bank, Cherry Hill, NJ

Responsibilities:

Utilized Tableau and Power BI to design interactive dashboards that provided insights into operational metrics for Experian’s technology services and data management operations.

Applied BigQuery to manage and analyze large datasets related to IT operations, including system performance and customer engagement data.

Integrated data from Dayforce into BI tools like Tableau and Power BI, creating HR dashboards that provided real-time insights into attendance, payroll, and compliance metrics.

Led the development of ETL processes for integrating data from multiple sources, ensuring timely and accurate reporting on operational metrics and system performance.

Translated IT operational data into actionable insights that improved business processes and enabled leadership to make data-driven decisions.

Performed performance tuning of SQL queries and procedures, improving overall database efficiency by 20%.

Led UAT and production deployments for IT reporting solutions, ensuring they met business requirements and provided clear value to stakeholders.

Developed key performance metrics for system uptime, data processing efficiency, and IT support, improving operational efficiency by 15%.

Leveraged Azure Data Factory for orchestrating and automating data workflows, enabling seamless data integration across cloud and on-premise environments.

Collaborated with IT and data engineering teams to optimize data workflows and enhance system performance tracking through automated reporting tools.

Automated operational reporting using SQL and BigQuery, reducing manual intervention and improving the speed of delivering insights to business teams.

Presented IT performance metrics to leadership, helping to set data-driven goals for system improvements and operational enhancements.

Conducted root cause analysis of system performance issues using decision science techniques, leading to a 20% reduction in downtime.

Developed IT system performance dashboards that tracked key metrics such as data processing times, customer support response rates, and server health.

Worked with cross-functional teams to identify areas for improvement in operational reporting, providing leadership with actionable insights to improve IT services.

Used decision science methodologies to predict system performance bottlenecks, improving proactive IT resource allocation.

Led ETL workflows for integrating operational and customer data into BigQuery, ensuring the accuracy and timeliness of IT reports.

Optimized SQL queries for IT reporting, reducing query execution times and improving report generation speed by 25%.

Presented new IT reporting solutions to non-technical stakeholders, ensuring they understood how to leverage insights for business decisions.

Developed dashboards that tracked operational metrics such as system uptime, data processing speeds, and customer service performance.

Applied data modeling techniques to improve system performance analysis, providing leadership with clearer insights into operational efficiency.

Supported IT leadership with data-driven insights that helped improve system reliability, service delivery, and customer satisfaction.

Led production deployments for IT operational dashboards, ensuring successful rollout and adoption across the business.

Tableau Developer/ Admin Oct 2021 to July 2022

Client: Huntington Bank, Columbus, OH

Responsibilities:

Collaborated with financial analysts and risk teams to understand data needs, delivering insights that improved fraud detection and compliance.

Developed and maintained data pipelines in Python and SQL, ensuring accurate integration of financial transaction data from multiple sources.

Applied machine learning algorithms and statistical models to detect patterns in banking data, reducing fraud by 15%.

Conducted exploratory data analysis using SQL and Python to uncover trends in financial transactions and improve risk management.

Built and maintained data models and data dictionaries to support regulatory compliance and internal audits.

Automated ETL processes using Python and PL/SQL to ensure accurate data extraction, transformation, and loading into financial systems.

Utilized Power BI and Tableau to create financial dashboards, providing real-time insights into banking operations.

Leveraged Oracle and SAP HANA for data warehousing, enabling scalable analysis of financial data across departments.

Monitored data quality and worked with data engineering teams to resolve discrepancies, improving data accuracy by 25%.

Applied regression and predictive modeling techniques to forecast financial trends and improve loan default predictions by 10%.

Leveraged Google BigQuery for analyzing large-scale financial transaction data, improving the speed and scalability of data processing by 30%.

Integrated BigQuery with SQL and Python to streamline data extraction, transformation, and loading (ETL) processes, ensuring accurate and timely integration of financial transaction data from multiple sources.

Designed and implemented BigQuery solutions to detect anomalies and fraud in banking operations, contributing to a 15% reduction in fraudulent activities through improved data processing workflows.

Partnered with data engineers to optimize BigQuery-based workflows, reducing query execution time by 25%, enabling faster decision-making for risk management and compliance teams.

Utilized BigQuery to create scalable data models for financial data, ensuring efficient reporting and enhancing the accuracy of financial forecasting by 10%.

Automated BigQuery pipelines to handle terabytes of financial data, reducing manual intervention by 35% and increasing the operational efficiency of the bank’s data management processes.

Supported cross-functional teams with data analysis to enhance fraud detection and risk mitigation strategies.

Developed automated reports using Python and SQL, reducing manual data processing efforts by 30%.

Conducted root cause analysis of financial data discrepancies, resolving issues and improving data quality across systems.

Developed and optimized complex SQL queries, stored procedures, and functions in SQL Server to streamline data extraction and reporting processes.

Partnered with data scientists to optimize financial models and improve risk assessment processes using Python and SQL.

Implemented data governance practices to ensure compliance with financial regulations and industry standards.

Provided training to junior analysts on the use of SQL and Power BI for financial data analysis and reporting.

Led data-driven projects focused on improving fraud detection systems, contributing to a 10% reduction in fraudulent transactions.

Regularly presented data insights and recommendations to senior executives, influencing financial risk management strategies.

Applied advanced statistical techniques to financial data, optimizing loan approval processes and reducing risk exposure.

Stayed current with trends in data science, machine learning, and cloud computing, applying new techniques to enhance banking data processes.

Tableau/ Power BI Developer July 2016 to July 2021

TCS, Hyderabad, India

Responsibilities:

Innovated and executed a variety of Power BI dashboards, strategically measuring Key Performance Indicators (KPIs) like Project Planning, Budgeting, Finance, and completion. Employed advanced metrics in the Data and DAX (Data Analytical Expressions) layer and Power BI M-query.

Collaborated extensively with multiple partner teams and delved into Key Research Areas (KRAs) such as Sales, Inventory, and Forecasting. This collaborative effort resulted in substantial service improvements and a remarkable 99.99% Data Accuracy in SLT reporting.

Actively contributed to the Snowflake migration project, where ETL logic transitioned into Snowflake stored procedures using JavaScript. Notably, SQL errors were systematically captured for further processing.

Demonstrated proficiency in applying advanced Snowflake SQL constructs, including array_agg and clustering.

Orchestrated coordination across diverse Customer Organizations, effectively transforming project milestones into successful project deliverables for numerous Power BI dashboards and Tableau Reports.

Implemented meticulous always-on documentation practices across multiple Power BI analytics efforts, ensuring data and requirement hygiene alongside operational standards across various entities.

Crafted and maintained sophisticated data flows in Tableau, ensuring optimal data visualization and reporting efficiency.

Utilized Tableau to engineer multi-level filtering and drill-through analyses within reports and dashboards.

Devised custom PowerShell scripts, enhancing Tableau's dynamic dataset refresh capabilities and ensuring data currency.

Leveraged Tableau for real-time sales monitoring, empowering the sales team to respond promptly to market trends and achieve quarterly revenue growth.

Seamlessly integrated Tableau with diverse data sources, including SQL Server, Excel, Oracle, and Azure, ensuring a comprehensive coverage of data.

Developed and maintained Snowflake schemas and tables, enabling data integration across multiple sources for advanced analytics.

Engineered intricate data flows within Power BI and Alteryx ETL solutions, showcasing adeptness in Power BI administration, including Gateway setup and data source configuration.

Demonstrated an in-depth understanding and expertise in leveraging DAX functions, encompassing the design of aggregations, calculated columns, Measures, etc., to support advanced reporting needs.

Spearheaded a telecom project focusing on network performance and customer analytics, utilizing advanced SQL and data modeling techniques.

Designed and developed Power BI dashboards to visualize network performance metrics, customer churn rates, and service quality KPIs.

Employed advanced analytics tools such as Python, R, Tableau, PowerBI, and SAS to extract insights from large telecom datasets.

Implemented ETL processes using Snowflake and Databricks, ensuring efficient data integration and processing.

Created macros linking telecom databases to PowerPoint and Excel files, enhancing data presentation and accessibility.

Utilized strong analytical skills to convert consumer insights and performance data into high-impact product initiatives.

Collaborated with telecom engineers and business stakeholders to identify key data sources and ensure accurate reporting.

Delivered insights that improved network performance, reduced customer churn, and enhanced overall service quality.

Designed and implemented scalable data pipelines in Snowflake to handle large datasets, ensuring high performance and real-time data accessibility.

Designed intricate multi-level filtering and Drill-through analyses within Power BI Reports and dashboards.

Developed custom PowerShell scripts, adding dynamism to Power BI's dataset refresh processes.

Implemented ETL processes using SSIS, ensuring data integrity and consistency.

Data Analyst (Intrenship) July2015 to June 2016

Micron Technologies INC Hyderabad, India

Responsibilities:

Directed the development and upkeep of sophisticated Data Engineering and Analytical applications, precisely designed for Sales, Inventory, and Point of Sale (PoS) divisions. Effectively used SQL and Tableau to extract meaningful insights.

Pioneered the creation of over 50 analytical workflows, ensuring seamless end-to-end fault tolerance and incremental data pipeline design. Employed advanced SQL constructs for efficient and robust data processing. Integrated data quality analyst expertise to ensure the accuracy and reliability of analytical outputs.

Played a key role in driving Sales and Inventory Analytics, contributing strategically to Personalization and Targeted Marketing initiatives. Conducted comprehensive data quality checks, maintaining high standards for data accuracy and reliability.

Provided valuable analytical insights for Sales Backlog tracking, Sales Performance, and Order Performance metrics. Leveraged a combination of Ad-hoc and persistent analytical functions using SQL seamlessly integrated with Tableau.

Collaborated effectively with internal and external teams, actively participating in the design of reporting frameworks, scorecards, and dashboards using the versatile capabilities of Tableau. Applied data quality analysis techniques to enhance the integrity of the data used in reporting.

Conducted thorough data analysis, profiling, and quality checks, utilizing advanced SQL queries and harnessing Tableau functionalities for enhanced visualization. Integrated data quality analysis practices to identify and rectify data discrepancies.

Demonstrated expertise in predictive analytics using Tableau, implementing AI models to identify patterns and trends for informed decision-making. Ensured the quality and reliability of data used in predictive analytics through continuous data quality assessments.

Automated configuration-driven ETL data flows in Azure Data Factory, ensuring seamless integration with Tableau and supporting various data source endpoints. Implemented data quality checks within ETL processes to guarantee the consistency of data.

Spearheaded backend Data Engineering platform simplification efforts, specifically tailored for Sales, Inventory, and Backlog order processes. Instituted robust data quality measures to enhance the accuracy and completeness of the data processed.

Implemented a range of performance metrics and KPIs, providing a holistic view of organizational health across all Divisions, utilizing SQL for precise measurement. Incorporated data quality metrics to assess and enhance the quality of the data used in performance measurements.

Fostered collaboration across diverse partner teams, including Business Continuity, Operational Health, Inventory, and Forecasting, significantly contributing to service improvements, and achieving 100% Data Accuracy fulfillment for various divisional reporting. Implemented data quality frameworks to ensure consistency and accuracy in reporting.

Architected a robust Tableau-based reporting solution for Sales and Inventory, supporting a geographically diverse customer base of over 250+. Utilized a backbone SQL Server Analysis Services (SSAS) tabular model, emphasizing analytics, personalization, and targeted marketing.

Led requirement gathering sessions with business users and cross-functional teams, ensuring a clear understanding of project needs. Managed the development of new features and capabilities, overseeing the delivery of Tableau dashboards and rich data visualizations. Integrated data quality analysis techniques to validate and enhance the reliability of data presented in dashboards.

Oversaw and built data pipelines, managed data ingestion processes, and played a key role in the development and maintenance of ETL processes and jobs. Incorporated data quality checks into data pipelines to identify and rectify data issues early in the process.

Leveraged and architected reporting platforms based on Near Real-Time (NRT) datasets, providing crucial real-time analytics for enhanced productivity. Integrated data quality monitoring to ensure the ongoing accuracy and reliability of real-time data.

Conducted scoping and estimation for new projects and enhancements, quantifying development and testing efforts for effective project planning. Implemented data quality assurance measures as part of project scoping to proactively address potential data issues.

Provided transparent project status updates, highlighting issues and opportunities, to project teams, leadership, and other stakeholders. Integrated data quality metrics into project status updates to communicate the reliability and accuracy of data used in project outcomes.

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