Sreekanth Patel
Sr. Business Data Analyst
*****************@*****.*** +1-561-***-**** https://www.linkedin.com/in/sreekanth-n-patel/
PROFESSIONAL SUMMARY:
• Results-driven Senior Business Data Analyst with over 7 years of experience transforming complex data into actionable insights across healthcare, retail, finance, and government sectors.
• Proven ability to build scalable dashboards, automate reporting pipelines, and generate KPIs for executive leadership using SQL, Power BI, Alteryx, Python, and cloud platforms.
• Well-versed in end-to-end data analytics and engineering responsibilities, including data storage and computing, batch and real-time data processing, orchestration, streaming, data warehousing, analytics, and machine learning across cloud, hybrid, and open-source ecosystems.
• Strong analytics expertise in SQL, Python, and BI tools (Power BI, Tableau, Looker, Qlik etc.), enabling data-driven insights through ETL/ELT pipelines, dashboards, and interactive reporting.
• Well-versed in data governance and regulatory compliance, including HIPAA, GDPR, SOX, PCI DSS, and FISMA ensuring high standards for data quality, security, and observability.
• Effective cross-functional collaborator with strong communication and project leadership skills, experienced in stakeholder engagement, agile delivery, and managing enterprise data platforms.
EDUCATION:
Master of Science (M.S) in Information Systems (Data and Business Analytics) August 2022 – December 2023
Florida International University (FIU)
Grade: A
Bachelor of Technology (B. Tech) in Electronics and Communication Engineering August 2016 – May 2019
Vidya Jyothi Institute of Technology (VJIT – Autonomous, Deemed University), Hyderabad, India
Grade: A
PROFESSIONAL EXPERIENCE
Client: State of Florida (Department of Transportation (FDOT))– Boca Raton, FL December 2023 – Present
Role: Senior Business Data Analyst
Responsibilities:
• Developed executive-level dashboards using Power BI, Tableau, and Excel (Power Query, PivotTables, DAX) to visualize toll collection performance, traffic flow trends, and payment behaviour.
• Developed and optimized complex SQL queries, CTEs, and materialized views in Snowflake to support daily reporting, business KPIs, anomaly detection, and strategic forecasting.
• Collaborated with cross-functional business teams to gather requirements and translate them into scalable, self-service BI solutions aligned with business goals and compliance standards.
• Conducted root cause analysis, trend analysis, and cohort segmentation to uncover insights into toll violations, billing discrepancies, and seasonal congestion patterns.
• Applied A/B testing, time series analysis, and diagnostic analytics to evaluate operational changes and pricing strategy effectiveness.
• Modeled datasets using Star and Snowflake schemas, enabling performance-optimized data exploration and consistent metric definitions across reports.
• Utilized SAS Connect to bridge mainframe (z/OS) and distributed environments, enabling seamless transfer of datasets for downstream analytics.
• Wrote JCL scripts to automate daily jobs, extract customer, claims, and transactional data from DB2 tables, and feed it into SAS for transformation and reporting.
• Designed KPI scorecards, slicer-based dashboards, and RLS-enabled views for different stakeholder groups (finance, operations, policy) using Power BI Service and Power BI Dataflows.
• Led data storytelling sessions, using clear narrative frameworks to guide stakeholders through insights and ensure business decisions were supported by actionable data.
• Leveraged Python (pandas, seaborn, matplotlib) for EDA, automation of reporting workflows, and building reusable analytics scripts for internal reporting pipelines.
• Used Hugging Face Transformers, BERT, and spaCy for NLP-driven insights from unstructured text (emails, support tickets, call transcripts), improving customer experience metrics and document classification.
• Integrated public vehicle registration APIs and license plate recognition feeds to enrich toll transaction data and enhance billing accuracy.
• Partnered with operations and finance teams to perform variance analysis, forecast modeling, and what-if scenario planning using historical data and projected traffic volumes.
• Created data profiling reports and validation routines to assess data quality, integrity, and completeness before dashboards and financial reports were distributed.
• Maintained and version-controlled all SQL logic, analytics code, and dashboard components using Git, Azure DevOps, and Confluence documentation standards.
• Supported CI/CD pipelines for analytics assets (SQL scripts, Python logic, dashboards), enabling faster and safer deployment of production-ready solutions.
• Facilitated Agile sprint ceremonies, led backlog grooming, and collaborated using Jira to prioritize and deliver analytics solutions in iterative cycles.
• Conducted workshops and one-on-one sessions to improve data literacy across business units, helping non-technical teams derive insights from reports.
• Ensured all analytics workflows met PII, PCI DSS, and SOC 2 compliance standards, with a strong focus on secure access and data governance.
• Utilized version control (Git), and Agile methodologies.
Environment: SQL (Snowflake, T-SQL), Power BI, Tableau, Python (pandas, seaborn, matplotlib), Excel, DAX, Power Query, Jupyter, Azure DevOps, Git, Jira, Confluence, Star/Snowflake Schema, SAS, DB2, A/B Testing, NLP (Transformers, BERT, spaCy), Business Process Modeling, Dataflows, Power BI Service, REST APIs, Agile/Scrum, PCI DSS, SOC 2, PII Compliance, Data Storytelling
Company: VIPoint Solutions Private Limited (Full Time) July 2021 – July 2022
Role: Senior Software Engineer
Responsibilities:
• Developed complex, cross-functional data analysis pipelines to support healthcare reporting and compliance analytics across claims, EHR, and provider data using Snowflake, Azure Synapse, and Databricks.
• Built reporting layers using Snowflake, Azure SQL Data Warehouse, and Delta Lake, enabling downstream Power BI, Tableau, and Alteryx dashboards for clinical quality, claims adjudication, and patient outcomes.
• Led the design of regulatory compliance dashboards (HIPAA, GDPR) tracking PHI data access, patient consent, and anomaly detection in claims data using Power BI, Looker, and custom DAX/MDX metrics.
• Performed deep-dive statistical analysis and hypothesis testing using Python (pandas, NumPy, SciPy) and R to uncover trends in readmission rates, fraud patterns, and chronic disease prevalence.
• Created OLAP cubes and semantic models using SSAS and LookML to support self-service BI, enabling business users to analyze key KPIs such as cost per case, claim denial rate, and care quality scores.
• Built automated data quality and profiling frameworks in Python and dbt, tracking completeness, uniqueness, and accuracy across datasets with interactive data validation reports.
• Partnered with actuaries and risk analysts to build predictive models and extract ML-ready features using SQL and Python, supporting use cases like claims fraud detection and patient churn.
• Designed and maintained JCL job streams to extract structured datasets (claims, member eligibility, billing transactions) from mainframe DB2 systems for downstream analysis.
• Used SAS Connect to establish secure sessions between SAS EG (Enterprise Guide) and mainframe environments, allowing efficient remote data manipulation and retrieval.
• Delivered actionable insights to business stakeholders through executive dashboards, Power BI bookmarks, and KPI cards, driving improvements in operational efficiency, claims throughput, and patient engagement.
• Designed data reconciliation and exception reports in Power BI, enabling teams to trace record mismatches across EHR, billing, and enrollment systems—reducing data resolution time by 60%.
• Conducted exploratory data analysis (EDA) on structured and semi-structured data (X12, HL7, JSON, XML) to guide data mapping, feature engineering, and downstream analytics pipeline design.
• Translated business requirements into SQL logic and visual metrics, ensuring alignment across business, actuarial, and clinical reporting teams.
• Collaborated with data governance teams to define and enforce data dictionaries, naming conventions, and column-level lineage using tools like Informatica, Collibra, and Power BI Data Catalog.
• Leveraged Fivetran, SSIS, and Azure Data Factory to enable secure, compliant ingestion of healthcare data across on-prem and cloud sources, supporting analytical reporting—not full-scale engineering.
• Built interactive Tableau dashboards for clinical insights and cost forecasting, using Level of Detail (LOD) expressions, table calculations, and dynamic filters to enhance user-driven analysis.
• Supported CI/CD pipelines for analytics workflows (Python scripts, dbt models, dashboards) using GitHub Actions, improving quality assurance and deployment traceability.
• Conducted real-time monitoring and insights reporting on streaming healthcare data using Power BI Streaming Datasets and Azure Event Hub integrations.
• Collaborated in Agile squads with product owners, clinicians, and analysts to iterate on reports, KPIs, and scorecards using Jira and Confluence for backlog management and release planning.
• Delivered data literacy training and dashboard walkthroughs for clinical operations and billing teams, empowering users to explore insights independently and improve data-driven decisions.
Environment: Power BI, Tableau, Looker/LookML, Alteryx, SSIS, SSAS, Azure Synapse Analytics, Snowflake, Delta Lake, SQL (T-SQL, Snowflake SQL, MySQL), Python (pandas, NumPy, seaborn, SciPy), R, Jupyter, dbt, Fivetran, Azure Data Factory, Azure SQL Data Warehouse, Power BI Streaming, SAS, DB2, GitHub Actions, Jira, Confluence, Informatica, HIPAA, GDPR, X12, HL7, XML/JSON, Data Governance, OLAP, Statistical Modeling, Predictive Analytics, Data Quality, Healthcare Analytics
Company: Bobcares (Full Time) June 2019 – June 2021
Role: Senior Software Engineer
Responsibilities:
• Designed and delivered interactive dashboards using Amazon QuickSight, Power BI, and Alteryx to visualize sales KPIs, marketing ROI, and inventory metrics, enabling real-time insights for merchandising and operations teams.
• Modeled integrated datasets in Amazon Redshift and Amazon Athena, supporting self-service BI across sales, CRM, and product data using star schemas, partitioning, and materialized views.
• Developed complex SQL queries and stored procedures to analyze product interaction trends, customer journey data, and fulfillment performance across multiple touchpoints.
• Built and maintained ETL/ELT workflows using AWS Glue, Fivetran, and Python, transforming data from PostgreSQL, MySQL, and Salesforce into analytics-ready formats stored in Amazon S3 and Redshift.
• Conducted A/B testing and marketing attribution analytics using Python (pandas, scikit-learn, NumPy) and SQL to evaluate campaign effectiveness and drive UI optimization decisions.
• Performed exploratory data analysis (EDA) and feature engineering using Python and Jupyter notebooks, supporting machine learning models for churn prediction and product recommendations.
• Consolidated data from disparate systems (ERP, CRM, clickstream, eCommerce) into a centralized S3-based data lake, enabling company-wide reporting, trend analysis, and executive KPIs.
• Built streaming analytics pipelines with Amazon Kinesis Data Streams and Kinesis Data Analytics, enabling real-time product availability dashboards and behavioral analysis.
• Leveraged SAS PROC SQL and data step programming to manipulate large DB2 datasets into analytics-ready formats with minimal manual intervention.
• Developed QuickSight dashboards with drill-downs, anomaly detection, custom visuals, and row-level security for sales teams, marketers, and operations executives.
• Led the creation of customer segmentation models, integrating purchasing behavior, RFM scores, and engagement data to improve personalized campaigns and upsell targeting.
• Automated data validation and integrity checks using SQL, PyTest, and custom Python scripts embedded into GitHub Actions CI/CD pipelines, ensuring trusted, production-ready analytics.
• Ensured data governance and compliance with GDPR/CCPA via IAM policies, Amazon Macie for sensitive data detection, Athena audit logs, and field-level encryption in Redshift Spectrum.
• Delivered training sessions and walkthroughs to improve data literacy, enabling business teams to interpret and explore insights independently using dashboards and ad hoc SQL queries.
• Integrated Power BI with Redshift and S3 to provide hybrid reporting layers and federated data access for users across finance, marketing, and customer service departments.
• Deployed machine learning models in Amazon SageMaker and Lambda for churn scoring, demand prediction, and click-through optimization, with outputs visualized in dashboards.
• Orchestrated data workflows using Apache Airflow on Amazon MWAA and monitored performance with CloudWatch, enabling SLA enforcement and anomaly detection in data delivery.
• Used Amazon S3 Lifecycle Rules and Intelligent-Tiering to optimize storage cost for archived product metadata, user event logs, and analytical outputs.
Environment: AWS (Amazon S3, Redshift, Athena, Kinesis, SageMaker, Glue, Lambda, CloudWatch, IAM, Macie, MWAA), SQL (PostgreSQL, MySQL, Redshift SQL), Power BI, Amazon QuickSight, Python (pandas, scikit-learn, NumPy), Alteryx, Jupyter, Salesforce, GitHub Actions, Fivetran, SAS, DB2, Apache Airflow, A/B Testing, Data Governance, Data Masking, CCPA/GDPR, Data Lake Architecture, Streaming Analytics, Churn Modeling, Real-Time Dashboards, Data Validation, RFM Segmentation, Customer Behavior Analysis, Star Schema, CI/CD, Jira, Confluence
Company: Vitara Information Technologies Pvt Ltd – Hyderabad July 2016 – May 2019 Role: Data Engineer/ Analyst
Responsibilities:
• Designed Power BI visualizations, including Bar, Clustered Bar, Area, Pie, Scatter, Cards, Multi-Row Cards, Table, Matrix, Gauge, and Slicer visualizations, improving data storytelling and business insights.
• Built a churn propensity scoring system by collaborating with data scientists to deliver transformed datasets into SAS Visual Analytics and R-based models, improving retention campaign targeting by 22%.
• Modeled telecom usage patterns using MapReduce jobs in Java, enabling advanced segmentation of customers based on call frequency, recharge patterns, and location-based trends.
• Integrated SAS Access to DB2 libraries for real-time querying and transformation directly on DB2 data—minimizing data movement and improving processing speed.
• Performed advanced SQL development in Oracle PL/SQL and SQL Server to generate intermediate staging tables, enabling high-performance joins and indexed views for visualization layers.
• Designed relational and dimensional data models using ERwin Data Modeler, ensuring referential integrity and consistency across source systems and reporting data marts.
• Implemented role-based access control (RBAC) and data masking within reports to protect customer PII data and comply with organizational data privacy protocols.
• Developed monitoring shell scripts and alerting logic for Hadoop job failures and SLA violations, ensuring workflow stability using Bash, Cron Jobs, and Zookeeper.
• Maintained source control with Git and managed release deployments via Jenkins, ensuring smooth delivery of code updates across development and production clusters.
• Documented data lineage, transformation logic, and SLA compliance using internal Wikis and Confluence, enabling full traceability for audit and troubleshooting.
• Used Flume for real-time ingestion of clickstream logs into HDFS, supporting exploratory behavior analysis and anomaly detection.
• Integrated Power BI with SQL Server, Data Marts, and Data Warehouses, ensuring efficient data retrieval and real-time reporting.
• Built and optimized ETL pipelines using SSIS to extract, transform, and load data from SQL Server, Oracle, CSV, Excel, and Text Files into structured data warehouses.
• Implemented data transformations such as Lookup, Multicast, Derived Column, OLEDB Command, and Data Conversion, ensuring high-quality data ingestion.
Environment: Hadoop (HDFS, MapReduce), Apache Pig, Hive, Sqoop, Oozie, Flume, Oracle PL/SQL, SQL Server, MicroStrategy, SAS Visual Analytics, Pentaho, ERwin Data Modeler, R, SAS, DB2, Shell Scripting, Bash, Cron Jobs, Zookeeper, Hortonworks Data Platform (HDP), Java, Git, Jenkins, Confluence, SSIS, Oracle, Toad