Vijaya Perumalla

Data & AI Engineer with 9+ years of experience, specializing in designing, building, and optimizing scalable data pipelines, enterprise feature stores, and MLOps infrastructure. Expertly leverages multicloud platforms, big data frameworks, and advanced ETL/ELT processes to orchestrate high-quality data pipelines for machine learning models and Generative AI applications. Drives impact through innovative MLOps and AI-assisted development workflows, enhancing data integrity, accelerating delivery velocity, and transforming complex data into actionable insights.

DATA & AI ENGINEER at DoorDash (2025-08 – 2026-05)

• Utilized Cursor AI and pioneered agentic AI-assisted development workflows for intelligent code generation, automated unit testing, and complex optimization of distributed ETL/data pipelines, improving delivery velocity.
• Built comprehensive data quality and validation frameworks implementing pre- and post-load validation rules to ensure data integrity for advertising and performance analytics pipelines.
• Configured Watchtower-based ML anomaly detection checks using YAML to proactively alert on quality deviations and drift within production datasets.
• Co-engineered scalable data pipelines using Python, SQL, and Snowflake to ingest unstructured and semi-structured interaction data from multiple upstream systems.

AZURE DATA ENGINEER at Remitly (2024-05 – 2025-07)

• Utilized Azure Databricks and Apache Spark for distributed processing of massive financial datasets, transforming raw transactional data into clean arrays optimized for analytics ingestion.
• Automated serverless data workflows via Azure Functions and Logic Apps to enable real-time, event-driven data ingestion into Azure Data Lake.
• Performed complex data modeling using Python, Scala, and SQL to synthesize uniform, high-fidelity datasets suitable for downstream predictive modeling.

AWS DATA ENGINEER at Electronic Arts (EA) (2023-09 – 2024-04)

• Designed and implemented serverless ETL pipelines using AWS Glue and AWS Lambda to ingest, clean, and structure high-velocity telemetry and game behavior data into Amazon S3 lakes.
• Managed and optimized Amazon EMR clusters running Apache Spark and Hadoop for large-scale batch analysis, directly supporting data scientists building customer insight and churn prediction models.
• Optimized data warehouse performance using Amazon Redshift and Redshift Spectrum to drastically lower query latencies for multi-terabyte analytical reports.

DATA ENGINEER at Citi Bank (2018-05 – 2023-03)

• Designed and implemented robust MLOps pipelines using Cloud Composer (Airflow), Cloud Build, and BigQuery ML, automating the entire lifecycle from data ingestion to model validation and deployment.
• Partnered with data scientists to construct highly optimized data structures inside Google BigQuery, seamlessly integrating machine learning algorithms for real-time fraud detection and customer risk forecasting.
• Built real-time stream processing engines utilizing Google Cloud Dataflow, Apache Beam, and Cloud Pub/Sub to process financial transactions instantaneously.

DATA ENGINEER at Genentech (2016-08 – 2018-04)

• Configured Kafka cluster distribution, partitioning, and replicated commit log services to maintain low-latency feeds for real-time streaming pipelines.
• Authored highly optimized PySpark and Hive queries on a distributed Hadoop cluster, engineering custom shuffle and sort parameters to overcome performance bottlenecks.
• Standardized application deployment cycles by packaging data workflows into Docker containers managed via Kubernetes clusters.

Bachelors Degree – Sri Mittapalli Institute of Technology for Women