Keerti Bafna
Principal Data Engineer @Zebra Technologies
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WORK HISTORY
Principal Data Engineer @Zebra Technologies
Toronto, ON, CA
Growth JourneyData Scientist to Principal Data EngineerSep 2019 | PresentArchitect and own enterprise-scale lakehouse platforms on Databricks supporting pricing, promotions, and demand forecasting systems for global retail clients. Career progression from Data Scientist to Principal Data Engineer, providing a strong foundation across both ML and data engineering.Led the implementation of a pricing and optimization platform processing millions of time series using PySpark, Delta Lake, and Delta Live Tables. Designed high-throughput ETL pipelines ingesting gigabytes of transactional data daily with strict SLAs and fault-tolerant design.Defined platform architecture, CI/CD standards, data quality frameworks, and observability practices adopted across multiple teams. Built a custom Databricks monitoring and cost attribution system using REST APIs, resulting in over $1M USD in annual cloud cost savings.Led migration of more than 10 projects from Kubernetes to Databricks, defining migration strategy and reducing cloud costs by over 15% on a $5M spend while ensuring zero production disruption.Mentored and led teams of 6–8 data scientists and engineers, conducted architecture reviews, and represented Data Engineering in leadership discussions influencing platform strategy and investment decisions.
EDUCATION
Visvesvaraya Technological University
Bachelor of Engineering - BE, Mechanical Engineering
ABOUT KEERTI BAFNA
Principal / Staff Data Engineer with 11+ years of experience building and scaling data and ML platforms across retail and consumer domains, including pricing, promotions, optimisation, and demand forecasting.I’ve grown from a Data Scientist to Principal Data Engineer, giving me a strong edge in operationalising ML systems at scale — from experimentation and modelling to production-grade data platforms supporting mission-critical decisions.In recent years, my focus has been on architecting lakehouse platforms on Databricks, handling terabytes of transactional data, and enabling high-performance, cost-efficient pipelines used by global retailers. I’ve led initiatives that significantly improved reliability, reduced latency, and delivered >$1M USD in cloud cost savings through platform optimisation and standardisation.I work closely with Data Science, ML, and Product teams to bridge research and production, ensuring forecasting and optimization systems are scalable, observable, and governed. I’ve also led teams of 6–8 data scientists and engineers, mentoring senior engineers and influencing platform standards across organisations.Recently, I’ve been exploring agentic AI systems to automate parts of the data engineering lifecycle — including metadata-driven ETL, schema extraction, and AI-assisted pipeline generation.I’m passionate about building durable data platforms that accelerate AI/ML initiatives while maintaining operational excellence.
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