ID.me operates a secure digital identity wallet offering identity proofing, multi-factor authentication, and group-affiliation verification, compliant with NIST 800-63-3 IAL2/AAL2 and used by government, healthcare, and retailers.
<div class="content-intro"><h2><strong>Company Overview</strong></h2> <p>ID.me is the next-generation digital identity wallet that simplifies how individuals securely prove their identity online. Consumers can verify their identity with ID.me 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 ID.me at 20 federal agencies, 45 state government agencies, and 70+ healthcare organizations. More than 600+ consumer brands use ID.me to verify communities and user segments to honor service and build more authentic relationships. ID.me’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. ID.me is committed to “No Identity Left Behind” to enable all people to have a secure digital identity. To learn more, visit <a href="https://network.id.me/">https://network.id.me/</a>.</p> <p><span style="font-weight: 400;">ID.me 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.</span></p> <p><span style="font-weight: 400;">At ID.me, 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 <a href="https://insider.id.me/candidate-ai-use-in-interviewing/" target="_blank">here</a>.</span></p></div><h3><strong>Role Overview</strong></h3> <p>ID.me is seeking a <strong>Staff Software Engineer - Data Platform </strong>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 <strong>reliability, scalability, and performance</strong> of the systems that move, process, and store data across the company.</p> <p>In this role, you’ll own and operate key data infrastructure components — including <strong>event streaming platforms, relational databases, and batch processing systems</strong> — 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 ID.me’s data ecosystem meets demanding operational, security, and regulatory requirements.</p> <p>This is a hands-on technical leadership role for a data infrastructure engineer who thrives at the intersection of <strong>distributed systems, platform engineering, and data operations</strong>.</p> <p>This role is based out of our <strong>Mountain View, CA</strong> office and requires <strong>full-time in-office attendance</strong>.</p> <h3><strong>Responsibilities</strong></h3> <ul> <li><strong>Own and operate core data infrastructure</strong>, including event streaming, relational database, and batch processing platforms.</li> <li>Design and implement <strong>highly reliable, observable, and scalable data systems</strong> that enable real-time and batch data processing.</li> <li>Develop automation and guardrails for <strong>data governance, retention, and compliance</strong>, ensuring auditability and consistency across services.</li> <li>Partner with application, platform, and SRE teams to improve <strong>data access patterns, reliability SLAs, and recovery processes.</strong></li> <li>Establish standards for <strong>data infrastructure monitoring, alerting, and capacity planning</strong>, ensuring proactive issue detection.</li> <li>Drive <strong>operational excellence</strong> by improving resilience, reducing toil, and implementing self-healing or automated recovery mechanisms.</li> <li>Evolve and optimize data pipelines that support downstream analytics, identity verification, and machine learning systems.</li> <li>Evaluate, implement, and operate <strong>event-driven and batch data platforms</strong> such as Kafka, Google Pub/Sub, Dataflow, or Temporal.</li> <li>Lead incident response and root cause analysis for production data systems, contributing to postmortems and platform improvements.</li> <li>Mentor engineers and advocate for reliability-focused engineering culture across teams.</li> <li><strong>Data lake architecture</strong> — Design and build the data lake storage and compute topology (object storage, partitioning, lifecycle, tiering) to support batch and streaming workloads.</li> </ul> <h3><strong>Minimum Qualifications</strong></h3> <ul> <li>Bachelor’s or Graduate degree in Computer Science, Software Engineering, or a related technical field.</li> <li>8+ years of professional experience in <strong>data engineering, software engineering, or distributed systems development</strong>.</li> <li>6+ years of programming experience in one or more languages such as <strong>Go, Python, or Java</strong>, with emphasis on automation and data system integration.</li> </ul> <p><strong>Preferred Qualifications</strong></p> <ul> <li>Deep expertise in <strong>building and operating data systems</strong>—including relational databases, streaming, and batch platforms—in production environments.</li> <li>Hands-on experience administering and optimizing <strong>PostgreSQL or other relational databases</strong> in the cloud (AWS RDS, CloudSQL, or AlloyDB).</li> <li>Solid understanding of <strong>reliability engineering principles</strong>, including observability, SLOs, capacity management, and operati