Job Overview
Location
Bangalore, India
Job Type
Full-time
Category
Data Engineer
Date Posted
March 19, 2026
Full Job Description
đź“‹ Description
- • As a Sr. Lead AI Engineer, Data at Coupa Software Inc., you will play a pivotal role in advancing the company’s AI-driven spend intelligence platform by designing and scaling the data infrastructure that powers machine learning models trained on trillions of dollars of global enterprise spend data. Your work will directly enable predictive, prescriptive, and automated insights that improve operating margins for businesses worldwide, making this a high-impact role at the intersection of AI, data engineering, and enterprise SaaS.
- • Day to day, you will architect and optimize large-scale data pipelines for ingesting, cleaning, and curating anonymized spend data from Coupa’s global network of 10M+ buyers and suppliers; collaborate with data scientists and ML engineers to define feature engineering strategies and data requirements for AI model training; implement scalable ETL/ELT processes using modern data tools to handle commodity classification, supplier normalization, and benchmark metric generation across 197+ enterprise tables; ensure data quality, lineage, and governance standards are met for AI/ML use cases; mentor junior engineers and contribute to technical architecture decisions that support the evolution of Coupa’s AI data foundation.
- • You will join a pioneering AI and data engineering team within Coupa’s broader technology organization, which is dedicated to transforming raw spend data into actionable intelligence through cutting-edge AI and machine learning. The team operates with a strong emphasis on innovation, collaboration, and technical excellence, leveraging cloud-native technologies and agile practices to deliver scalable solutions that drive measurable business outcomes for customers across industries.
- • In this role, you will deepen your expertise in large-scale data systems for AI, gain hands-on experience with petabyte-scale enterprise data workflows, and contribute to breakthroughs in AI-powered spend management. You’ll have the opportunity to shape the future of Coupa’s AI capabilities, work on problems of significant scale and complexity, and grow as a technical leader in a high-growth, innovation-driven enterprise SaaS environment.
🎯 Requirements
- • Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related technical field
- • 8+ years of experience in data engineering, with proven expertise in building and scaling large-scale data pipelines for analytics or machine learning
- • Strong proficiency in SQL, Python, and experience with distributed computing frameworks such as Apache Spark or Flink
- • Hands-on experience with data warehousing platforms (e.g., Snowflake, BigQuery, Redshift) and ETL/ELT orchestration tools (e.g., Airflow, Dagster)
- • Demonstrated ability to work with semi-structured and structured data at scale, including data modeling, transformation, and quality assurance
- • Experience collaborating with data science and ML teams to prepare training datasets and support feature stores
🏖️ Benefits
- • Comprehensive health, dental, and vision insurance plans for employees and dependents
- • Generous paid time off, including vacation, sick leave, and company holidays
- • Retirement savings plan with company matching contributions
- • Opportunities for professional development, including access to learning platforms, conferences, and technical training
- • Hybrid work flexibility with options for remote collaboration and office-based engagement in Bangalore
Skills & Technologies
Data Science
Senior
Onsite
About Coupa Software Inc.
Coupa Software provides a cloud-based Business Spend Management platform that unifies procurement, invoicing, expense management, sourcing, contract lifecycle, supplier risk, payments, treasury, and analytics into a single user experience. It automates workflows, enforces policy and compliance, delivers real-time visibility, and leverages AI-driven insights to help global enterprises control spend, improve margins, and reduce risk across direct and indirect goods, services, and contingent labor.
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