Coupa2mo ago

Sr. Lead Software Engineer - 11282

US RemoteRemoteMid Levelsenior

OtherData EngineeringSoftware EngineeringLead Software EngineerData Platform Software Engineer

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Overview

Requirements Summary

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,…

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awsazurebigquerygcppythonsnowflakesqlcode-reviewdatabase-designetlmentoring

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?

🔹 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.

🔹 Collaborative Culture: We value collaboration and teamwork, and our culture is driven by transparency, openness, and a shared commitment to excellence.

🔹 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.

The Impact of a Sr. Lead Software Engineer at Coupa:

At Coupa, we are building a centralized, modern data platform on Apache Iceberg, designed to operate across a multi-cloud environment and power customer-facing analytics at scale. Our platform processes large volumes of data every day, combining batch ingestion and CDC-based streaming events, and serves data that is directly consumed by our customers.

This is a senior, hands-on individual contributor role with high ownership and technical depth. A key part of the role is continuously improving latency, reliability, and performance as the platform scales.

You will work on challenging, real-world data problems—ranging from high-throughput ingestion to enabling sub-second data retrieval and query performance—while influencing architecture and best practices across the broader data ecosystem. If you enjoy solving complex data platform problems, working close to production systems, and seeing your work directly impact customers, this role offers constant opportunities to make a meaningful impact without becoming repetitive.

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

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.