Resume

Summary

VINEET REDDY SADDI

Analytics Engineer with 2+ years of experience building scalable data models, transforming operational data, and delivering trusted analytics assets. Proficient in SQL, dbt, Snowflake, Looker, Python, and Power BI, with a strong focus on reliable data pipelines, dimensional modeling, and consistent business metrics.

Pursuing an MS in Business Analytics at the University of Massachusetts Amherst (2024–2026), focusing on analytics engineering, data modeling, and business decision optimization.
Worked as an Analytics Engineer at Harman (JBL) – Bhagya Services (2022–2024), designing scalable dbt models, transforming service operations data, and standardizing KPI definitions across teams.
Certified in Microsoft Power BI Data Analyst Associate (PL-300), Stanford Machine Learning Specialization, Google Advanced Data Analytics, and Google Project Management.

Certifications

Power BI Data Analyst Associate – Microsoft Certified

Hands-on experience in Power BI for data modeling, DAX, and interactive dashboards.

Machine Learning Specialization – Stanford University

Hands-on experience with neural networks, regression, classification, and evaluation methods.

Google Advanced Data Analytics

Developed skills in statistical modeling, Python analytics, and predictive analysis.

Google Project Management

Training in Agile project management, stakeholder coordination, and risk analysis.

Data Analysis with Python – Zero to Pandas

Completed real-world projects using Pandas, Matplotlib, and data visualization.

Education

Master of Science in Business Analytics

Aug 2024 – Dec 2026

University of Massachusetts Amherst, USA

Focused on real-world analytics engineering, data modeling, and decision-driven data storytelling through hands-on projects.

Work Experience

Analytics Engineer – Harman (JBL) | Bhagya Services

July 2022 – June 2024

Hyderabad, India

Built and maintained scalable dbt models using table, view, and incremental materializations to transform warranty claims, device lifecycle, and technician data.
Architected layered dbt framework using staging and mart models, defining sources and column-level tests in YAML for warranty status validation, turnaround time calculations, and device lifecycle tracking.
Developed and documented 20+ dbt models leveraging ref() dependency management and reusable Jinja macros, implementing schema tests to enforce data integrity and reduce metric discrepancies by 35%.
Optimized complex SQL transformations in Snowflake on high-volume service transaction tables containing 200,000+ monthly warranty records, reducing dashboard refresh latency by 40%.
Strengthened data definitions for turnaround time, first-time fix rate, and technician productivity metrics, aligning KPI logic across Service Operations, Finance, and Quality stakeholders.
Led root cause analysis of upstream claim status mismatches using reconciliation queries across device intake and closure tables, preventing three production reporting failures and reducing resolution time by 25%.
Designed Looker dashboards visualizing device turnaround time, warranty rejection percentage, and daily submission volumes, enabling operational visibility across 12 regional service centers.
Automated recurring service performance reporting workflows using Looker, eliminating manual claim-level consolidations and improving monthly reporting efficiency by 30%.