Senior Engineer, Identification

Accountabilities: Design, develop, and optimize backend services for real-time data processing and web APIs, prioritizing reliability, latency, scalability, and cost efficiency. Build and operate scalable distributed systems that support growing traffic and increasing platform demands while maintaining high service availability. Take ownership of service-level indicators (SLIs) and service-level objectives (SLOs) for core API services, using error budgets to balance development speed with system stability. Develop and maintain observability capabilities, including metrics, structured logging, distributed tracing, dashboards, and alerts focused on meaningful user-impacting issues. Participate in an equitable, scheduled on-call rotation, responding to production incidents, mitigating impact, and helping restore service reliability. Lead blameless postmortems, document incident findings, and implement corrective actions to prevent recurring failures. Reduce operational overhead through automation, process improvements, and reliable operational tooling. Conduct capacity planning, load testing, and performance tuning to anticipate demand and ensure systems scale safely and efficiently. Build, maintain, and improve production infrastructure using cloud platforms, containerization, orchestration, infrastructure-as-code tools, and CI/CD pipelines. Collaborate with cross-functional teams to deliver integrations, support product requirements, and improve the reliability of identification services. Mentor other engineers, share technical knowledge, and contribute to engineering standards and best practices. Take features and improvements from initial design through deployment, monitoring, and ongoing production ownership. Requirements At least 5 years of backend software development experience, including substantial responsibility for operating production systems. Strong hands-on experience with Go (Golang), scalable API development, microservices, and real-time data processing. Demonstrated expertise in designing, scaling, and maintaining distributed systems in production environments. Practical experience with Docker, Kubernetes, and infrastructure-as-code tools such as Terraform or AWS CloudFormation. Experience operating cloud-based services, preferably on AWS, with an understanding of production deployment and reliability practices. Working proficiency with observability platforms such as Datadog or comparable tools, including instrumentation, dashboards, metrics, logging, tracing, and alerting. Prior on-call experience, including incident triage, impact mitigation, troubleshooting, and post-incident documentation. Strong SQL skills and experience with databases or data stores such as DynamoDB, Redis, or Elasticsearch. Proficiency with Git, shell scripting, and continuous integration and continuous delivery (CI/CD) workflows. Ability to work independently in ambiguous environments, communicate directly with stakeholders, and make sound technical decisions. Strong written and verbal English communication skills for effective collaboration in a globally distributed team. Willingness and ability to mentor junior engineers and contribute to a collaborative engineering culture. A bachelor's or master's degree in Computer Science, or equivalent practical experience. Preferred qualifications: Experience with analytical data platforms such as ClickHouse, Snowflake, BigQuery, Redshift, or Databricks. Familiarity with queuing and streaming technologies such as Amazon SQS or Kafka. Experience with performance profiling and cost optimization for high-throughput cloud workloads. Production experience with vector databases such as Pinecone or Qdrant. Background in machine-learning-adjacent product engineering, concurrent programming, or internet security and privacy mechanisms. Technology stack: Go, AWS, Terraform, Docker, Kubernetes, CI/CD pipelines, Datadog, ClickHouse, and dbt. Benefits Salary: Annual base salary range of $152,000–$205,000 for eligible U.S. locations, depending on geography, experience, and expertise. Location-based compensation: Illustrative annual salary ranges include $178,000–$205,000 for New York City and $152,000–$174,000 for Chicago. Remote work: Fully remote work environment, with team members working across a wide range of locations. Pay transparency: Compensation is benchmarked against comparable growth-stage companies and considers role, seniority, and geographic location. Inclusive culture: Commitment to fostering a respectful, supportive workplace that values diverse experiences, perspectives, and backgrounds. Professional development: Opportunities to mentor engineers, collaborate with experienced technical teams, and contribute to complex, high-scale systems.