Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Staff Backend Graph Engineer
- Remote from
- USA
- Salary
- USD 225k–250k / yr
- Department
- Software Engineering
- Employment
- Full Time
- Experience
- Senior
- Published
- Apply before
- 7 Dec 2026
- Listing views
- 31
- Application actions
- 2
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About this role.
Why Socure?
Socure is building the identity trust infrastructure for the digital economy — verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day.
We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won’t be your place. If you want to help build the future of identity with a team that holds a high bar for itself — keep reading.
About the Job
Build the graph that decides who’s real. Every day, Socure’s Identity Graph helps businesses decide whether to trust the person on the other side of a screen. It links billions of signals (names, addresses, devices, emails, phones) into one picture of identity, and it does this in milliseconds. Fraudsters move fast. The graph has to be faster.
As a Staff Engineer on this team, you’ll set the technical direction for the platform behind that decision. You’ll own hard problems end to end, work across Engineering, Data Science, Product, and Security, and see your work shape the products that protect millions of people.
What You Will Do
Shape the graph. Define the schemas, relationships, and ontology that make identity data consistent and extensible.
Own entity resolution at scale. Decide how records, people, devices, and addresses connect, and how confident we are, across data that is messy, sparse, and sometimes adversarial.
Build for speed and volume. Design batch and real-time pipelines, low-latency APIs, and serving systems that hold up under heavy load.
Power fraud intelligence. Ship the production systems behind our ML features, model inference, and graph analytics.
Move the numbers. Improve performance, reliability, and cost, and prove it with measurements.
Set the direction. Lead architecture decisions, document the tradeoffs, and carry initiatives from first design through production rollout.
Raise the bar. Mentor engineers and set the standard for design reviews, testing, CI/CD, and operations.
Protect identity data. Build privacy, security, and PII protection into every system from day one.
Your toolkit: Java, Scala, or Python. Spark, Kafka, Flink, Airflow, and AWS, including EMR. Graph technologies like Neo4j and Amazon Neptune.
What You Will Bring
Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field
7+ years building and running distributed systems in the cloud, with at least 2 at Staff or Senior Staff scope, or the equivalent
Production scale you can quantify. You can tell us what you built, how big it was, and how much you improved latency, reliability, or cost
Deep Spark, Kafka, or Flink experience in both batch and streaming, including debugging them in production
Low-latency serving. You have built and tuned APIs where milliseconds matter
Graph or entity-resolution experience. You have modeled connected data, matched records across noisy sources, or built an identity graph. Fraud or identity verification is a strong plus
Cloud and operations fluency. AWS, Kubernetes, infrastructure as code, CI/CD, and real production observability
ML data pipelines on Databricks, SageMaker, or similar, plus orchestration with Airflow or equivalent
Technical leadership. You have led ambiguous, cross-team work, mentored engineers, and changed the direction of a system through design docs and influence, not authority
What This Job is Not
It’s not a role for someone who hasn’t owned a system end to end in production, including being on call for it.
It’s not feature work on platforms someone else designed.
It’s not a defined backlog. You’ll frame ambiguous problems yourself.
It’s not a place to treat data privacy as a compliance checkbox.
Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
If you need an accommodation during any stage of the application or hiring process—including interview or onboarding support—please reach out to your Socure recruiting partner directly.
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Role DNA
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Core skills
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Sample interview questions
I would combine deterministic matching rules with probabilistic features and confidence scoring, retaining provenance for every edge and match decision. The design would include thresholds for automated merges, review or quarantine paths for uncertain links, continuous evaluation against labeled outcomes, and safeguards to prevent fraud-driven graph poisoning.
I would explain the initial throughput and failure characteristics, identify bottlenecks through metrics and tracing, then describe targeted changes such as partitioning, state management, serialization, back-pressure controls, or compute-rightsizing. I would quantify the outcome with before-and-after latency, error-rate, availability, and cost measures.
I separate immutable event capture from materialized serving views, use idempotent processing and durable offsets, and explicitly define acceptable consistency semantics for each consumer. For critical identity decisions, I would provide versioned records, audit trails, reconciliation jobs, and monitoring for delayed or conflicting updates.
I would begin with access-pattern analysis and SLOs, then design precomputed or cached neighborhood views for common traversals while keeping the authoritative graph and event stream durable. The service would use bounded queries, connection and cache management, horizontal scaling, load testing, and detailed observability to protect tail latency.
I would create a concise design document that states the problem, constraints, alternatives, risks, operational impact, and measurable success criteria. I would gather input early from affected teams, make tradeoffs explicit, align on ownership and rollout stages, and use results from a phased implementation to validate the decision.
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