Senior Engineer, Platform & Data

Own and scale the distributed ingestion and job systems that pull customers' GTM data into the platform: full loads and incremental sync, correct and idempotent across many tenants and sources. Design and build the multi-tenant data platform the agents reason over and write back through: the canonical data model and its evolution, the serving and query layer, and the contracts other services depend on. Build for scale and concurrency from the start: many tenants, large enterprise orgs, hundreds of concurrent jobs and agent reads, with latency, cost, and correctness under failure as first-class concerns. Own the reliability and observability of your services: instrumentation, tracing, SLOs, and the operational health of the data plane. Keep the platform source-pluggable: extend ingestion to new systems behind a clean adapter contract without rewriting the engine. Partner directly with the SVP of Engineering on platform and data architecture, including schema evolution, survivorship, and entity resolution at scale, and set the patterns the team builds services on.

Responsibilities

Own and scale the distributed ingestion and job systems that pull customers' GTM data into the platform: full loads and incremental sync, correct and idempotent across many tenants and sources; Design and build the multi-tenant data platform the agents reason over and write back through: the canonical data model and its evolution, the serving and query layer, and the contracts other services depend on; Build for scale and concurrency from the start: many tenants, large enterprise orgs, hundreds of concurrent jobs and agent reads, with latency, cost, and correctness under failure as first-class concerns; Own the reliability and observability of your services: instrumentation, tracing, SLOs, and the operational health of the data plane; Keep the platform source-pluggable: extend ingestion to new systems behind a clean adapter contract without rewriting the engine; Partner directly with the SVP of Engineering on platform and data architecture, including schema evolution, survivorship, and entity resolution at scale, and set the patterns the team builds services on

Requirements

5+ years building production backend and distributed systems, with deep ownership of at least one of: high-throughput data pipelines, job or workflow systems, or multi-tenant data platforms; Strong, current Python (TypeScript a plus) and you write and review production code; Distributed-systems fundamentals: concurrency, idempotency, consistency, partitioning, retries, and failure recovery, and you have owned the correctness and uptime of data-intensive services in production; Strong cloud-native application development: containerized services, queues and workers, object storage, and relational data at scale (AWS or equivalent). This is a development role, not an infrastructure or SRE role: you build and operate your own services; Strong relational data modeling and SQL at scale (Postgres a plus) and you've owned a production database schema and its evolution; High agency: you scope, prioritize, ship, and operate without waiting for permission

Nice to have

Worked against Salesforce or another large, messy enterprise data source (Bulk 2.0, REST, Pub/Sub, CDC); Built multi-tenant isolation (Postgres + RLS), master-data management, entity resolution or survivorship, or watermark-based incremental sync; Experience with durable execution (Temporal, Inngest) and the modern eval/observability stack (Langfuse, Braintrust), though our stack is intentionally lean; A founder or founding-engineer background, or built a data platform 0-to-1

Benefits

LeanData covers employee insurance premiums up to 90%; Stock options in LeanData for all full-time employees; Flexible PTO; 401K plan; Lunch provided on Mondays and Wednesdays