Sai Teja
Professional Summary
Data Analyst with 5+ years of experience delivering analytics, ETL, and predictive solutions across insurance, healthcare, banking, and manufacturing environments. Expert in SQL and Python for data modeling and pipeline automation, with hands-on experience operating Snowflake, Amazon Redshift, and Google BigQuery for centralized analytics. Built and scaled ETL using Apache Airflow and Talend and operationalized data ingestion with AWS Glue and Apache NiFi. Designed executive and self-service dashboards in Tableau, Power BI and Looker to inform underwriting, clinical operations, and finance stakeholders. Implemented ML solutions using scikit-learn, TensorFlow and PyTorch to support claims severity, readmission risk and credit scoring. Own end-to-end analytics delivery including data quality, deployment automation with Git/GitHub Actions and governance using Collibra and Informatica Data Catalog.
Technical Skills
Work Experience
- Collected, consolidated, and modeled claims, policy and telematics datasets using SQL and Python to enable underwriting and pricing analyses.
- Normalized JSON claim payloads from vendor REST APIs using Python to enable schema-on-read ingestion into the analytics layer.
- Engineered daily ETL workflows with Apache Airflow to ingest S3 sources and populate Snowflake for enterprise reporting.
- Designed executive dashboards in Tableau and Looker to surface loss ratios, claim trends, and underwriting KPIs for senior stakeholders.
- Developed production-ready deep learning models for unstructured claim notes using PyTorch and Python to enhance fraud signal detection.
- Processed large-scale event data using Apache Spark and Hadoop to support near-real-time operational reporting for claims teams.
- Automated data validation and deployment steps with PowerShell, Git, and GitHub Actions to enforce reproducible analytics pipelines.
- Documented runbooks and metadata in Confluence and tracked analytics sprints in JIRA to improve governance and handoffs.
- Extracted and transformed clinical and financial data from Oracle using SQL and Informatica to feed downstream reporting environments.
- Built automated ingestion flows with Apache NiFi and Jenkins to integrate EHR, billing, and scheduling feeds into the analytics platform.
- Developed interactive dashboards and executive reports in Tableau, Power BI and Excel to visualize length-of-stay, throughput, and utilization.
- Applied Python and R for outcome modeling and readmission risk analysis to support population health and operational improvement work.
- Managed Amazon Redshift datasets and orchestrated population using AWS Glue and S3 to provide centralized access for analysts.
- Implemented data governance and lineage with Collibra and Informatica Data Catalog to support HIPAA compliance and audit readiness.
- Cleansed and modeled transactional and financial datasets with SQL and Pandas to prepare regulatory reports for finance and risk teams.
- Orchestrated batch pipelines using Talend to load curated feeds into Google BigQuery for analytics consumption.
- Developed Tableau and Power BI dashboards to present risk exposure, profitability and KRI insights to finance stakeholders.
- Processed large transaction volumes with Hadoop and Apache Spark to support reconciliation and audit activities.
- Validated credit-scoring models using scikit-learn and SAS to support segmentation and risk monitoring.
- Authored NumPy-based aggregation pipelines to accelerate regulatory extract generation and reconciliation processes.
- Delivered executive reports via Google Data Studio using BigQuery extracts for stakeholder distribution.
- Analyzed manufacturing and operational datasets with SQL and Python to generate weekly performance reports for business units.
- Performed statistical analysis in R to identify production quality trends and prioritize process improvement opportunities.
- Built automated ETL jobs using Alteryx to streamline ingestion and reduce manual reporting effort across plants.
- Developed management dashboards in Tableau and Power BI to track supply chain KPIs and inventory levels.
- Processed semi-structured telemetry with MongoDB and integrated results into reporting pipelines for long-term analytics.
- Created exploratory visualizations with Matplotlib and Seaborn to support root-cause analyses and executive presentations.
- Built interactive product dashboards using Plotly for web-based stakeholder reviews and ad-hoc analysis.
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