Staff Backend Engineer - Trust & Control Safety Systems

Database architecture: Redesign and optimize data models, queries, and indexing strategies across MongoDB, Firestore, and ElasticSearch

Search at scale: Own ElasticSearch reliability — ingestion, indexing, shard strategy, and query performance for billions of documents

Reliability & performance: Eliminate bottlenecks, improve replication health, and enforce predictable query and index latency

System design: Define data flow and integration boundaries between storage, cache, and APIs with clear contracts and fault isolation

Observability: Build visibility into every data path — metrics, traces, slow query logs, replication lag, and cluster health dashboards

Testing & quality: Make testing non-negotiable — enforce unit, integration, and load testing standards across all backend modules

Engineering culture: Drive RFCs, ADRs, and design reviews that push clarity and precision. Codify patterns that make good engineering repeatable

Hands-on leadership: Write code, design systems, and debug production issues. Lead by technical example, not by delegation

Mentorship & influence: Level up engineers around you — reviews that teach, feedback that sticks, and systems that outlive individuals

Heavyweight Engineering Pedigree: 8+ years of experience building high-scale, mission-critical backend systems where p99 regressions are unacceptable.

Distributed Systems Mastery: Deep expertise in concurrency, event-driven architectures, and consistency primitives (CAS, atomic dry-runs, idempotency).

Financial Rigor: Experience with systems where code correctness has direct financial impact—metering, ledgers, or payment gateways.

Operational Instinct: A track record of technical leadership through influence, driving architecture for multi-team platforms from design to production.

Experience with systems where correctness has financial consequences — metering, ledgers, billing, reservations, or comparable transactional domains

Strong experience with relational and NoSQL data models (especially with complex transactional data), plus working knowledge of streaming and analytical stores for time-windowed aggregation

Experience designing policy- or rules-driven systems that change through configuration rather than deploys — versioning, safe rollout, and rollback of behavioural change

Sound judgement on the enforcement trade-off : how to set thresholds and actions that stop real harm without punishing legitimate customers, and how to measure whether you got it right

Familiarity with monitoring, SLOs, and root-cause analysis in production environments

Familiarity with hallucination mitigation strategies (e.g., code constraints, test scaffolding, embeddings, context injection)

Track record of technical leadership without formal authority — driving cross-team architecture to a decision, writing designs others build from, and lifting the engineering standard around you

Excellent communication and cross-functional collaboration skills, including with Finance, Risk and Support stakeholders

A pragmatic, hands-on engineer who balances long-term vision with iterative execution

Experience with ElasticSearch cluster management, multi-tenant indexing, or lifecycle policies

Familiarity with ClickHouse or other OLAP systems for analytics

Background in event-driven systems, data pipelines, or message brokers

Contributions to open-source or public technical writing on database performance or systems design

Abuse & Fraud Expertise: Experience building rules engines, velocity checks, or adversarial defense systems at massive scale.

Modern Data Stack: Hands-on with high-throughput tools like Go, Redis (Lua), ClickHouse, and Flink for real-time aggregation.

Hyper-Growth Scaling: Experience navigating the transition from early architecture to load-bearing platform at scale.

Exposure to stream processing at scale (Flink, Kafka Streams, Beam) and columnar analytical stores (ClickHouse, BigQuery) for real-time aggregation

Hands-on experience with Node.js and Go in production services, particularly on latency-sensitive or high-throughput paths

Experience working with AI Tools, Billing & Metered Systems

Contributions to open-source tools, internal platforms, or engineering blogs