Meta
6+Hive data sources built from scratch to track metadata accuracy across 13+ signals and 5+ Instagram ad events.
Portfolio / Data Engineering
Reliable data systems for products that move at scale.
Vaibhavi Kundle builds ETL pipelines, quality frameworks, and analytics workflows that help teams trust their data and act on it faster. Her work spans Meta, ZS Associates, and graduate research at Arizona State University.
Selected Impact
Work measured in speed, reliability, and business lift.
Performance
~40%Lower query latency from partitioning, bucketing, and dimensional modeling on large-scale ad interaction data.
ZS Associates
100Accurate monthly business insights generated through SQL-driven ETL workflows for client sales performance.
Quality
70%Reduction in incorrect insights and system failures through Python and PySpark data validation on HDFS.
Professional Experience
Building trustworthy data platforms across product and consulting environments.
Meta
Data Engineer InternWorked on revenue-critical Instagram ads data systems, from source modeling to observability and analytics feedback loops.
- Architected and deployed 6+ Hive data sources from scratch, defining schemas, keys, and dimensional models to track metadata accuracy across 13+ signals and 5+ critical Instagram ad events.
- Designed end-to-end ETL pipelines with Python, SQL, Hive, Presto, and Airflow to process petabyte-scale ad interaction logs and cut query latency by about 40 percent.
- Implemented an automated reliability layer with 15+ SQL checks, null monitoring, and alerting to proactively detect regressions and maintain SLA compliance across analytics pipelines.
- Developed 3+ UniDash dashboards on event coverage, metadata health, and compute-to-revenue ROI, uncovering a 30 percent signal logging gap that informed logging fixes and infrastructure decisions.
- Partnered with product, data science, and infrastructure teams to translate ad delivery requirements into production-grade data platforms.
- Python
- SQL
- Hive
- Presto
- Airflow
- UniDash
ZS Associates
Data EngineerDelivered client-facing analytics systems and operationalized data workflows with a strong emphasis on correctness and efficiency.
- Engineered ETL workflows using SQL to generate and publish about 100 accurate monthly business insights tied to client sales performance.
- Reduced the risk of incorrect insights and system failures by 70 percent by developing Python and PySpark scripts for data checks on Hadoop HDFS.
- Cut processing time from 60 minutes to 20 minutes through SQL query optimization and broader stability improvements in the insights pipeline.
- Created a reusable Python template for the data processing architecture, reducing manual configuration by 60 percent and accelerating multi-project delivery.
- Automated software testing suites, reducing manual effort by 45 percent and doubling testing speed while supporting cross-project handovers.
- SQL
- Python
- PySpark
- Hadoop HDFS
- Testing Automation
ZS Associates
Data Engineer InternSupported analytics delivery, quality validation, and client-facing operations during the transition into full-time engineering work.
- Improved data integrity through 40+ quality checks, SQL optimization in AWS Redshift, and interactive Tableau dashboards.
- Built pipelines using SQL and Excel to generate insights for clients while leading operational runs and demos.
- Resolved about 70 percent of issues in real time during client sessions and documented internal processes for smoother delivery.
- Received the Rising Star award for strong performance and contributions to the Insights team.
- SQL
- AWS Redshift
- Tableau
- Excel
Projects
Research and applied work beyond production systems.
A technical foundation across machine learning, natural language processing, and computer vision complements Vaibhavi's data engineering work.
Academic project / 2022
Video CAPTCHA Using VQA, NLP, and Deep Learning
Developed a video-based CAPTCHA system for user authentication that combines computer vision, natural language processing, and deep learning techniques.
- Integrated sentiment analysis and used CNN and LSTM models to validate responses with 98 percent accuracy.
- Published the work at the 2022 IEEE-ICAST Conference.
Education
Graduate depth in scalable systems, backed by strong fundamentals.
Aug 2024 - May 2026
Master of Computer Science
Arizona State University, United States
Relevant coursework: Data Processing at Scale, Cloud Computing, Data Mining and Visualization
Aug 2018 - Jun 2022
Bachelor of Technology in Information Technology
K. J. Somaiya College of Engineering, Mumbai, India
Relevant coursework: Data Structures and Algorithms, Data Analytics, Database Management, Machine Learning
Technical Toolkit
Languages, data systems, and ML libraries used in real projects.
Languages
- Python
- SQL
- JavaScript
- HTML
- CSS
- C
Libraries
- NumPy
- Pandas
- Keras
- Scikit-learn
- Matplotlib
- OpenCV
- SciPy
Tools
- Airflow
- Hive
- PySpark
- Presto
- MySQL
- Tableau
- AWS
- Hadoop
- Django
- Docker
- Git
Contact
Open to building data products with rigor and measurable impact.
Reach out for data engineering, analytics, or machine learning opportunities.