Senior Engineer - Identification

We are seeking an experienced Senior Software Engineer to help build and operate the end-to-end fraud detection systems behind our Identification APIs. Your primary responsibility will be building highly performant backend systems and keeping them reliable at scale. You'll write the services and you'll own how they behave in production.

This role requires strong experience in backend software development, particularly API development and scaling existing systems in Go, paired with a genuine interest in reliability engineering. You'll design and implement scalable, high-performance backend systems, maintain their SLOs, and take ownership of features from concept through deployment and steady-state operation.

A key aspect of this role involves a deep understanding of scaling and operating distributed systems, allowing us to multiply our capacity safely and efficiently as our user base continues to grow rapidly.

Responsibilities

• Design, build, and optimize backend systems for real-time data processing and web services, treating reliability, latency, and cost as first-class constraints.
• Own SLIs and SLOs for Identification services, using error budgets to balance shipping velocity against stability.
• Build the observability that makes systems debuggable — metrics, structured logging, distributed tracing, and alerts that fire on user-visible symptoms, not noise.
• Share an on-call rotation, respond effectively to incidents, and lead blameless postmortems. Schedules are set in advance, with equitable coverage and minimal off-hours disruption.
• Reduce operational toil through automation, and close out incident action items so the same failure doesn't recur.
• Run capacity planning, load testing, and performance tuning so the platform scales ahead of demand.
• Partner with cross-functional teams on integrations, and mentor other engineers as they grow.

Qualifications

• 5+ years in backend development, including meaningful time operating what you built in production.
• Strong, demonstrated experience with Go, and with scalable APIs, real-time data processing, and microservices.
• Hands-on experience running production services with Docker and Kubernetes, and with IaC tools like Terraform or CloudFormation.
• Working fluency with observability tooling (Datadog or comparable) — instrumenting services and building useful dashboards and alerts.
• On-call experience: triaging live incidents, mitigating impact, and writing up what happened.
• Strong SQL, plus experience with databases like DynamoDB, Redis, or Elasticsearch.
• Proficiency with Git, shell scripting, and CI/CD, and fluency in English for a global, remote team.
• Able to work independently in ambiguous environments with direct stakeholder contact, and eager to mentor junior engineers.
• BS/MS in Computer Science or equivalent experience.
• Bonus: analytical storage systems (ClickHouse, Snowflake, BigQuery, Redshift, Databricks), queueing and streaming systems (SQS, Kafka), or performance profiling and cost optimization of high-throughput cloud workloads.
• Bonus: vector databases (Pinecone, Qdrant) in production, ML-adjacent product engineering, concurrent programming, or a strong grasp of internet security and privacy mechanisms.

Tools and Technologies:

• Backend development: Golang
• Infrastructure and delivery: AWS, Terraform, Docker, Kubernetes, CI/CD pipelines.
• Observability: Datadog, metrics/logging/tracing.
• Data analytics/processing: ClickHouse, dbt.

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Compensation & Transparency

At Fingerprint, we believe in pay transparency — it's part of how we operate as a distributed, remote-first team.  Salaries are based on function, level, and geographic location, benchmarked against similar stage growth companies. 

Because this role is open across the United States and can be based anywhere our team members work from, the salary range reflects that geographic breadth: $152k - $205k

Some example salaries for specific locations are below:

NYC: $178k - $205k

Chicago: $152k - $174k

Final offer amounts are determined by multiple factors, including the successful candidate's location, experience, and expertise, and may vary from the amounts listed above.