Coupa makes margins multiply through its community-generated AI and industry-leading total spend management platform for businesses large and small. Coupa AI is informed by trillions of dollars of direct and indirect spend data across a global network of 10M+ buyers and suppliers. We empower you with the ability to predict, prescribe, and automate smarter, more profitable business decisions to improve operating margins.
Why join Coupa?
馃敼 Pioneering Technology: At Coupa, we're at the forefront of innovation, leveraging the latest technology to empower our customers with greater efficiency and visibility in their spend.
馃敼 Collaborative Culture: We value collaboration and teamwork, and our culture is driven by transparency, openness, and a shared commitment to excellence.
馃敼 Global Impact: Join a company where your work has a global, measurable impact on our clients, the business, and each other.
Learn more on
Life at Coupa blog and hear from our employees about their experiences working at Coupa.
What You鈥檒l Do
Design and implement scalable, high-throughput data ingestion systems that integrate internal and external data across domains
Design and build core data platform components, including ingestion, validation, orchestration, and lineage, with a focus on code quality and reliability
Build and evolve a centralized data lake using Apache Iceberg (or similar table formats)
Work across multi-cloud environments (AWS, GCP, Azure) to design and implement cloud-agnostic data ingestion and processing patterns
Contribute hands-on to the Semantic layer, ensuring data is easy to consume for BI and analytics teams
Partner with Senior Data Engineers, Platform Engineers, and Analytics Engineers to align how data is produced, stored, and consumed
Establish practical engineering standards for testing, observability, and operational excellence
Provide technical leadership through mentorship, code reviews, and design discussions, while remaining hands-on
What You Will Bring to Coupa
8-10+ years of experience in software or platform engineering, with a focus on building scalable data and analytics platforms
Strong understanding of data ingestion patterns at scale, including CDC, and how data should be modeled and stored in a data lake for fast, efficient retrieval
Proven experience building and operating large-scale data pipelines in production
Experience working with modern data warehouses such as Databricks, BigQuery, or Snowflake
Strong proficiency in Python and SQL, with a focus on writing production-quality, maintainable, and testable code
Hands-on experience working with cloud data services in AWS, GCP, or Azure
Experience working with query engines such as Presto or Trino to enable fast, reliable analytics over data lakes
Familiarity with Lakehouse architectures and table formats such as Iceberg or Delta Lake
Familiarity with data governance, lineage, metadata, cataloging, and data quality practices
Nice to have:
Nice to have exposure to semantic layers, metrics frameworks, or BI-friendly data modeling
Experience supporting analytics or AI/ML workloads
Candidates could be based out of any of these locations: San Francisco, Seattle, New York, Austin, Chicago, Phoenix, Northern Virginia (Fairfax, Arlington, Richmond) OR remote in the US.