Original listing text, shown exactly as published by the company.
What You’ll 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.
