Staff Software Engineer (Data Infrastructure)

Tome; Cambridge; On-site; GBP 110,000 - 160,000; Full time

Benefits offered by this job

Meaningful equity

3 weeks PTO

Health insurance

11 paid holidays

Team dinners and offsites

Wednesdays work from home

Family leave (3 months)

401k plan

Commuter and lunch stipend

Job summary

Tome is seeking a Staff-level Software Engineer, Data Infrastructure, to build the next generation of data systems close to product and customers. You will work across backend, infrastructure, and product-facing data problems with a focus on scalable, schema-flexible data models and reliable analytics foundations.

Based in Cambridge (UK) or San Francisco, you’ll help evolve our data architecture from a pragmatic foundation to best-practice designs, including change data capture and event

Qualifications

• 7+ years experience owning production data systems with data freshness and correctness impact.
• Experience with large-scale, high-volume data infrastructure in a high-growth product environment.
• Strong HLD skills in data modeling, schema evolution, and query optimization.

Responsibilities

• Scale the analytics serving path behind customer-facing dashboards while ensuring data freshness and isolation.
• Design ingestion paths, event models, and schemas for transactional-to-analytical workflows.
• Define boundaries between OLTP and OLAP, balance reliability and performance, and guide architecture.

Skills

PostgreSQL

Data infrastructure

Distributed systems

Event pipelines

Query performance

Schema design

Observability

Production data systems

Education

Bachelor's degree in a relevant field

Tools

Kafka

Flink

Spark

Iceberg

Postgres

Job description

• We’re building the infrastructure foundation for a fast-growing AI product company serving thousands of customers:
• Our Postgres fleet serves 5B+ queries a month — roughly 2,500 QPS steady state, with sustained spikes past 25,000 QPS — and database workload more than doubled last month
• Redis sustains ~50,000 commands per second behind a job platform that executes 10M+ background job runs a day across ~170 queues
• We ingest tens of millions of emails and calendar events a month
• That growth creates scaling pressure across backend systems, infrastructure, and data infrastructure
• We’re hiring a staff-level engineer who spikes in data infrastructure but is excited to work across backend systems, infrastructure, and product-facing data problems
• The work is close to the product, close to customers, and close to production
• As a Software Engineer, Data Infrastructure, you’ll build the next generation of our data systems
• We got remarkably far on a deliberately simple stack: Postgres as the system of record, a sharded transactional outbox for change events, Redis-buffered sync into Typesense for search, BullMQ for processing, and Postgres-backed customer-facing analytics with per-organization row-level security
• The next phase is evolving that pragmatic foundation into best-practice data architecture: change data capture, event modeling, schema design, query performance, freshness guarantees, and the right boundary between transactional and analytical workloads
• The system of record itself is unusual
• Customers define their own objects, attributes, and relationships at runtime, so the core data model is a schema-flexible, graph-shaped store: entity-attribute-value with typed edges, versioned attribute values, and relationship history
• That makes schema design, indexing, and query performance genuinely hard problems rather than routine tuning
• The surface area is wider than analytics: customer-facing dashboards, historical and audit data, datasets that power pipeline-generation products, and evaluation data that measures our AI agents
• This is data infrastructure work, not a BI or dashboarding role
• It’s a good fit for someone who likes high-volume data systems, pragmatic architecture decisions, and building foundations that product and engineering teams can actually depend on
• This role can be based in San Francisco or Cambridge. In San Francisco, you’d work from our HQ alongside the founders and most of the engineering team. In Cambridge, you’d join an initial group of staff-level engineers at our new, infrastructure-focused Kendall Square site, working alongside one of our most senior infrastructure engineers. We aim to build the site and organization around this group as the company scales
• Scale the analytics engine behind customer-facing dashboards, tackling query performance under row-level security, workload isolation, read architecture, and observability as data volume grows
• Design the ingestion paths, event models, schemas, and query patterns that move data from transactional writes into search, dashboards, and history—with clear guarantees around freshness, correctness, replay, and failure recovery
• Evolve our schema-flexible, graph-shaped data model so customer-defined objects, attributes, and relationships remain fast to query as their size and complexity grow
• Build the foundations for historical reporting and auditability, including attribute versioning, relationship history, and change capture
• Build reliable data systems for usage metering, pipeline generation, and AI evaluation, where errors have direct customer, product, or financial consequences
• Decide when our existing architecture remains the right foundation and when new analytical, streaming, or workflow systems earn their added complexity
• Set the technical direction, abstractions, ownership boundaries, and engineering practices for data systems as the company grows
• What your first year looks like
• Scaling the analytics serving path behind customer-facing dashboards is the anchor project, but the work stays close to the product. The current slate also includes:
• Zero-downtime schema migrations for an 18-collection Typesense search deployment
• A usage-metering pipeline for consumption billing
• Historical and audit data modeling
• Evaluation data infrastructure for our AI agents
• Expect a first year that mixes foundational data systems with product-shaped projects, with the scope to own the technical direction for how data is modeled, moved, and served across Lightfield—and to shape the architecture, abstractions, and team we build as the company scales
• You’d be building our analytical data architecture from close to the beginning — the foundations are deliberately simple, and the architecture that scales them is yours to shape. Customer-facing data products are on the roadmap, database workload more than doubled last month, and the foundations you build will carry the company for years

Benefits

• Meaningful early equity
• 3 weeks of PTO
• Health insurance (medical, dental, vision)
• 11 paid company holidays + we enjoy a winter holiday break
• Regular team dinners, events, offsites, and retreats
• Wednesdays work from home
• 3 months of paid family leave
• 401k plan
• Other perks include: commuter and lunch stipend

Clear communication, strong ownership, and a bias toward practical tradeoffsStrong software engineering fundamentalsGood judgment about when to make a tactical fix and when to invest in a more durable platform or architecture changeProduct orientation: you care about how data infrastructure decisions affect customers, users, and engineering velocity7+ years experienceExperience owning production data systems where query plans, replication lag, backfills, data freshness, schema evolution, or data correctness had real user-facing consequencesComfort debugging across multiple layers of the stackClickHouse, OLAP systems, event pipelines, data warehouses, or analytical infrastructurePostgres at scale, and the boundary between OLTP and OLAP systemsKafka, Flink, Spark, Iceberg, or similar streaming and lakehouse systemsAPIs, queues, workflow systems, and distributed systemsObservability, incident response, service ownership, and production debuggingData for ML/AI systems: enrichment pipelines, eval harnesses, or data-quality toolingExperience in a high-growth product environment