Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I’m a senior data analytics professional who enjoys turning messy, large-scale enterprise data into reliable reporting and actionable insights. I’ve worked across financial services, healthcare, and telecommunications, partnering with stakeholders to clarify requirements, map data elements across systems, and deliver governed datasets that teams can trust. My day-to-day strengths include advanced SQL (CTEs, window functions, analytics), scalable processing with Python and PySpark, and building interactive Tableau dashboards for KPI and trend monitoring. I also support production reliability through data quality checks, reconciliation, ETL pipeline monitoring with Azure Data Factory and Apache Airflow, and SQL-based validations during QA and UAT—so business decisions stay accurate and timely.…I’m a senior data analytics professional who enjoys turning messy, large-scale enterprise data into reliable reporting and actionable insights. I’ve worked across financial services, healthcare, and telecommunications, partnering with stakeholders to clarify requirements, map data elements across systems, and deliver governed datasets that teams can trust. My day-to-day strengths include advanced SQL (CTEs, window functions, analytics), scalable processing with Python and PySpark, and building interactive Tableau dashboards for KPI and trend monitoring. I also support production reliability through data quality checks, reconciliation, ETL pipeline monitoring with Azure Data Factory and Apache Airflow, and SQL-based validations during QA and UAT—so business decisions stay accurate and timely.

SAI VISHNU TEJA MDATA ANALYST

Data Engineer, Data Scientist, Data Analyst, +1





I’m a senior data analytics professional who enjoys turning messy, large-scale enterprise data into reliable reporting and actionable insights. I’ve worked across financial services, healthcare, and telecommunications, partnering with stakeholders to clarify requirements, map data elements across systems, and deliver governed datasets that teams can trust. My day-to-day strengths include advanced SQL (CTEs, window functions, analytics), scalable processing with Python and PySpark, and building interactive Tableau dashboards for KPI and trend monitoring. I also support production reliability through data quality checks, reconciliation, ETL pipeline monitoring with Azure Data Factory and Apache Airflow, and SQL-based validations during QA and UAT—so business decisions stay accurate and timely.…I’m a senior data analytics professional who enjoys turning messy, large-scale enterprise data into reliable reporting and actionable insights. I’ve worked across financial services, healthcare, and telecommunications, partnering with stakeholders to clarify requirements, map data elements across systems, and deliver governed datasets that teams can trust. My day-to-day strengths include advanced SQL (CTEs, window functions, analytics), scalable processing with Python and PySpark, and building interactive Tableau dashboards for KPI and trend monitoring. I also support production reliability through data quality checks, reconciliation, ETL pipeline monitoring with Azure Data Factory and Apache Airflow, and SQL-based validations during QA and UAT—so business decisions stay accurate and timely.

Available to hire

I’m a senior data analytics professional who enjoys turning messy, large-scale enterprise data into reliable reporting and actionable insights. I’ve worked across financial services, healthcare, and telecommunications, partnering with stakeholders to clarify requirements, map data elements across systems, and deliver governed datasets that teams can trust.

My day-to-day strengths include advanced SQL (CTEs, window functions, analytics), scalable processing with Python and PySpark, and building interactive Tableau dashboards for KPI and trend monitoring. I also support production reliability through data quality checks, reconciliation, ETL pipeline monitoring with Azure Data Factory and Apache Airflow, and SQL-based validations during QA and UAT—so business decisions stay accurate and timely.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

Senior AI/ML Engineer at Mastercard

February 1, 2024 - Present

Addressed inconsistencies across payment transaction, customer, and operational datasets by partnering with stakeholders to understand reporting needs and define analytical solutions. Authored complex SQL using joins, CTEs, window/analytical functions, and aggregates to analyze high-volume payment data, improving performance and reducing report generation time. Led data mapping and transformation documentation across multiple source systems to standardize downstream ETL inputs. Strengthened data quality governance with dataset profiling, reconciliation, anomaly detection, and production issue resolution. Used PySpark for large-scale analysis, transformation optimization, and output validation prior to deployment. Built Tableau dashboards for KPIs, payment trends, and operational metrics, and supported QA/UAT with SQL validation scripts. Automated and monitored ETL execution with Azure Data Factory and Apache Airflow, improving pipeline visibility and reducing manual operational effort.

AI/ML Engineer at FIS

October 1, 2021 - December 31, 2023

Resolved reporting challenges caused by fragmented financial and payment data by collaborating with stakeholders to establish scalable data solutions and deliver trusted datasets for operational reporting. Produced complex SQL queries (joins, CTEs, window/analytical functions, aggregates) to analyze payment and customer information, optimizing query execution to improve turnaround time. Performed end-to-end data mapping across payment gateways, operational databases, and enterprise applications, documenting transformation logic to ensure consistent ETL processing. Designed and validated logical data models to support enterprise reporting and regulatory compliance, coordinating with data engineers on standardized structures. Used PySpark for profiling, anomaly detection, and transformation validation across large datasets. Implemented data quality governance including validation rules, reconciliation, exception handling, and root cause analysis to reduce recurring defects. Delivered int

Machine Learning Engineer at Complex Care

April 1, 2020 - August 31, 2021

Improved enterprise healthcare reporting by analyzing patient, claims, and provider data from multiple clinical systems and delivering datasets that support operational and regulatory decision-making. Wrote complex SQL with joins, CTEs, analytical functions, and aggregates to validate business rules and improve reporting efficiency. Conducted detailed data mapping across EHR, claims, and operational databases, maintaining documentation to streamline ETL and ensure consistent integration. Built scalable data models unifying patient, provider, and claims information for downstream analytics and reporting. Used PySpark for profiling, transformation validation, and anomaly detection at high volume. Strengthened data quality governance with reconciliation, validation checks, and governance standards, and resolved production inconsistencies using root cause analysis. Developed Tableau dashboards for patient trends, KPIs, and claims analysis. Supported manual/functional/regression/UAT with SQ

Data Scientist at AT&T

October 1, 2018 - February 29, 2020

Partnered with business stakeholders to address reporting challenges from fragmented customer, billing, and network data across enterprise systems. Analyzed requirements and delivered integrated datasets to improve reporting accuracy and operational decision-making. Created complex SQL queries using joins, subqueries, CTEs, analytical functions, and aggregates to extract, reconcile, and validate business data, improving efficiency and reducing turnaround time. Completed data mapping by identifying key data elements across source applications and documenting transformation rules for ETL. Designed logical data models and improved aggregation strategies for customer analytics, operational reporting, and BI. Used PySpark to identify anomalies, validate transformation logic, and optimize processing within enterprise pipelines. Implemented data quality validation, reconciliation, and exception handling; performed root cause analysis on production issues to minimize recurring defects. Develop

Python Developer at Reliance

March 1, 2016 - June 30, 2018

Supported reporting for sales, inventory, and supply chain operations by analyzing data from multiple operational systems and delivering reliable datasets for day-to-day decision-making. Wrote SQL queries with joins, aggregates, subqueries, and filtering to extract, validate, and analyze transactional data, improving consistency by resolving discrepancies. Assisted with data mapping (source-to-target relationships) and validated critical data elements to align ETL with business rules. Helped develop ETL workflows to extract, transform, and load data into centralized repositories for reporting and analytics, verifying completeness, consistency, and compliance. Used Apache Spark and Python for data profiling and transformation validation prior to deployment. Participated in manual/functional/integration/UAT using SQL validation scripts and collaborated with QA on defect resolution. Supported scheduling and monitoring with Apache Airflow and AWS Glue and contributed to production support,