Staff Engineer & Technical Lead – Ontology

We are looking for a staff-level engineer to lead the Ontology team as a hands‑on technical lead.

This role provides an opportunity for someone to take the technical leadership for the beating heart of our platform – which enables us to answer client questions in a highly scalable way. The key responsibility is to formalize Parable’s enterprise schema (ontology) and continuously hydrate it from raw data – establishing gold‑standard sources, identity resolution, and robust linking. You will collaborate deeply with Raw Data (connectors/ingestion), Context Mining (research → generalized pipelines), and the App team (APIs powering client UX).

This is an IC technical‑lead role (no direct reports to start). As the team grows, you’ll have the option—but not the requirement—to transition into people management or continue as a technical lead.

Parable’s mission is to make time matter.

We provide CEOs of companies with 1,000+ employees with deep observability of the time spent by their team across all strategies, projects, and processes. Our insights help teams focus on the work that matters the most, and drive data-driven decisions in resource allocation.

The company was founded by seasoned founders, with multiple 9-figure exits under their belts, and reached $2m of ARR within 6 months of going to market. Parable raised $17 million from investors like HOF Capital and Story Ventures, as well as 50+ founders and executives.

On the technical side, we are building an operating system for large enterprises – ingesting silo’ed data from across the workplace stack, shaping it into a strongly opinionated enterprise ontology, and contextualizing it to extract insights for clients. Each customer runs in a fully isolated, single-tenant GCP environment (own VPC, compute, storage, and KMS), with shared, parameterized pipelines instantiated per tenant—no bespoke schema per client.

Our platform stack includes Cloud Run Jobs/Compute Engine, Pub/Sub, Cloud Storage, Memorystore, BigQuery/Cloud SQL, and an Iceberg-based lake; we build primarily in Python/TypeScript (plus Rust).

Parable’s Ontology is our opinionated approach to what information and concepts exist in an Enterprise that can be extracted and structured out of the raw data – so that we can offer a productized, scalable, repeatable solution to our clients. The mission of the Ontology team is to maintain and expand this ontology so that, one day, all Client questions can be answered directly from it. This team maintains the balance between abstraction and specificity so that the Ontology can be productized for all clients.

• Owning the ontology – define entities/relationships, version data contracts, and balance abstraction vs. specificity so the model is reusable across clients.

• Hydrating and linking – design idempotent, incremental pipelines that unify identities across systems into stable Parable IDs and persist them as lakehouse tables.

• Establishing “gold sources” & standards – e.g., Workday for employees; accounting for customers; then link out to other systems for complete coverage.

• Productizing research – partner with Context Mining to transform one‑off client solutions into continuous, reusable ontology pipelines.

• Exposing the ontology – co‑design stable, performant APIs and query surfaces that power client UX (e.g., Company Explorer for headcount/payroll and tool‑usage views).

• Partnering upstream – specify connector mappings, entity keys, and documentation that help Raw Data ship taps faster with clearer downstream contracts.

• Raising the bar – drive technical standards (testability, observability, lineage/validation) so ontology jobs are dependable and easy to operate per‑tenant.
  

• 8+ years building data‑heavy systems with schema design, identity resolution, and event pipelines (idempotent, incremental, testable).

• Proven technical lead impact: multi‑quarter technical leadership, cross‑team influence, dependable delivery, and risk management.

• Strong Python and SQL; comfort across cloud data platforms and containers; experience designing APIs and data contracts used by product teams.

• Data‑quality and observability mindset (validation, lineage, run health) in production pipelines.

• Experience with cloud data lakes (Delta Lake or Apache Iceberg) on Parquet; BigQuery/Cloud SQL; Redis; Pub/Sub.

• Strong in Python or Rust or TypeScript in production services; interest in shaping evaluation for enrichment/agentic steps alongside Context Mining.