Love Is A Parable

Staff Engineer, Technical Lead – Ontology

Love Is A Parable

full-time

Posted on:

Origin:  • 🇺🇸 United States

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Salary

💰 $250,000 - $350,000 per year

Job Level

Senior

Tech Stack

ApacheBigQueryCloudGoogle Cloud PlatformPythonRedisRustSQLTypeScript

About the role

  • Lead the Ontology team as a hands‑on technical lead and take technical leadership for Parable’s enterprise schema (ontology)
  • Formalize entities/relationships, version data contracts, and balance abstraction vs. specificity for reusable models across clients
  • Design idempotent, incremental pipelines that unify identities across systems into stable Parable IDs and persist them as lakehouse tables
  • Establish gold-standard sources & standards and link out to other systems for complete coverage
  • Productize research by partnering with Context Mining to transform one‑off client solutions into continuous, reusable ontology pipelines
  • Co-design stable, performant APIs and query surfaces that power client UX and partner with App team
  • Specify connector mappings, entity keys, and documentation to help Raw Data ship taps faster with clearer downstream contracts
  • Drive technical standards (testability, observability, lineage/validation) so ontology jobs are dependable and easy to operate per‑tenant

Requirements

  • 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