Staff Software Engineer - Data Platform

Company Overview

is the next-generation digital identity wallet that simplifies how individuals securely prove their identity online. Consumers can verify their identity with  once and seamlessly login across websites without having to create a new login and verify their identity again. Over 152 million users experience streamlined login and identity verification with  at 20 federal agencies, 45 state government agencies, and 70+ healthcare organizations.

More than 600+ consumer brands use  to verify communities and user segments to honor service and build more authentic relationships. ’s technology meets the federal standards for consumer authentication set by the Commerce Department and is approved as a NIST 800-63-3 IAL2 / AAL2 credential service provider by the Kantara Initiative.  is committed to No Identity Left Behind to enable all people to have a secure digital identity.

To learn more, visit (Use the "Apply for this Job" box below)..

is a full-time, in‑office culture. Unless a specific job description explicitly states otherwise, all roles are on‑site five days per week at one of our offices in McLean, VA;
Mountain View, CA;
New York City, NY; or Tampa, FL. Certain roles — such as field-based sales or other remote‑by‑design positions — may have different work arrangements as noted in their individual postings.

At , we embrace the thoughtful use of AI tools in our daily work and there are even occasions where we leverage AI in our hiring process. However, during the interview process, we want to understand your individual skills and experiences. Therefore, we have guidelines on how AI can be appropriately used during your application and interviews which can be found here.

is seeking a Staff Software Engineer - Data Platform to lead the design, build, and operation of the core data infrastructure that underpins our identity platform. This engineer will be responsible for ensuring the reliability, scalability, and performance of the systems that move, process, and store data across the company.

In this role, you’ll own and operate key data infrastructure components — including event streaming platforms, relational databases, and batch processing systems — while driving automation and engineering best practices that improve data platform reliability and developer efficiency. You’ll partner closely with Platform Engineering, Site Reliability Engineering, and Compliance teams to ensure ’s data ecosystem meets demanding operational, security, and regulatory requirements.

This is a hands‑on technical leadership role for a data infrastructure engineer who thrives at the intersection of distributed systems, platform engineering, and data operations
.

This role is based out of our Mountain View, CA office and requires full‑time in‑office attendance
.

• Own and operate core data infrastructure
  , including event streaming, relational database, and batch processing platforms.
• Design and implement highly reliable, observable, and scalable data systems that enable real‑time and batch data processing.
• Develop automation and guardrails for data governance, retention, and compliance
  , ensuring auditability and consistency across services.
• Partner with application, platform, and SRE teams to improve data access patterns, reliability SLAs, and recovery processes
  .
• Establish standards for data infrastructure monitoring, alerting, and capacity planning
  , ensuring proactive issue detection.
• Drive operational excellence by improving resilience, reducing toil, and implementing self‑healing or automated recovery mechanisms.
• Evolve and optimize data pipelines that support downstream analytics, identity verification, and machine learning systems.
• Evaluate, implement, and operate event‑driven and batch data platforms such as Kafka, Google Pub/Sub, Dataflow, or Temporal.
• Lead incident response and root cause analysis for production data systems, contributing to postmortems and platform improvements.
• Mentor engineers and advocate for reliability‑focused engineering culture across teams.
• Data lake architecture — Design and build the data lake storage and compute topology (object storage, partitioning, lifecycle, tiering) to…