Planned hiring
Data Engineer
Lucy is a proactive AI coach from Lucy Labs that lives on your desktop as a cloud-backed app.
Planned hiring. This description explains the work we are preparing to hire for; it is not a confirmed start date or offer.
Build the data paths that let Lucy understand work without losing where its evidence came from or how it must be protected. Your scope connects desktop observations to downstream processing and keeps public tools-and-techniques knowledge flowing into the Knowledge Graph. Reliable transport, storage and lifecycle make both paths usable and accountable.
Your mission
Build the data paths that let Lucy understand work without losing where its evidence came from or how it must be protected. Your scope connects desktop observations to downstream processing and keeps public tools-and-techniques knowledge flowing into the Knowledge Graph. Reliable transport, storage and lifecycle make both paths usable and accountable.
What you will build
- Define typed ingestion contracts with Desktop, Behavior and downstream consumers. Carry user and organization boundaries, source identifiers, timestamps, data class and lineage through validation, normalization, ordering and schema changes.
- Implement idempotent ingestion, bounded retries, replay and backfills. Handle duplicate, late, malformed and partially processed records so recovery does not silently create new observations or corrupt downstream state.
- Build the Tools & Techniques Repository (TTR) ingestion path for approved public sources: fetching, extraction handoffs, validation, review queues and recoverable Knowledge Graph synchronization. Expose freshness, rejected items and failed syncs to operators.
- Maintain storage interfaces and migrations for evidence and reusable knowledge. Keep public-source knowledge separate from customer-private material, and provide auditable paths for any permitted promotion between boundaries.
- Implement data-class-specific retention and deletion propagation with Product Security and system owners. Trace source and derived records so replay, caches or downstream copies do not resurrect deleted data. Keep Content and Signal/Context retention policies distinct.
- Monitor throughput, lag, completeness, errors and cost. Provide usable diagnostics without copying sensitive payloads into logs, and own pipeline tests, operational documentation and recovery exercises.
Your partners and boundaries
The Knowledge Graph engineer owns entity meaning, ontology, semantic deduplication and semantic acceptance; you own transport, physical storage interfaces and propagation. AI Engineering supplies extraction runtime and Evaluation tests its quality. Analytics Engineering owns reusable metric transformations once those duties transfer. Behavior owns context interpretation. Neither TTR ingestion nor a catalog entry approves a tool for a customer; organizational tool policy remains an independent control.
What you bring
You can demonstrate robust pipelines using SQL and Python, TypeScript or comparable languages. You understand schema evolution, distributed failure, replay, lineage and data quality well enough to explain how a damaged run recovers. You have implemented access boundaries and retention or deletion across multiple stores. Experience with event processing and external-source ingestion is useful. We value practical judgment about when a simple recoverable pipeline is enough and when additional infrastructure is justified.
Your first 90 days
Agree one observation contract and one bounded public-source ingestion slice with their consumers. Prove duplicate-safe replay, schema rejection and recoverable synchronization on representative examples. Trace a deletion request through the selected source and derived stores with Security and QA. Publish operational measures, ownership and an explicit list of lifecycle gaps, leaving the next engineer able to reproduce the evidence and operate the path.
Next step
Express your interest
Email people@lucylabs.ai with the role title, a short introduction and relevant CV or portfolio. This is planned hiring; we will confirm current availability and the individual working arrangement.
Lucy Labs confirms the working arrangement, employer, location, eligibility, compensation and terms for each role. Our mailing address does not determine the job location or guarantee remote employment, worldwide eligibility or visa sponsorship.
Do not send passwords, government identifiers or bank details. Read the application privacy notice.
Frequently asked questions
Is this role open at Lucy Labs?
Lucy Labs lists this role as planned hiring. Availability depends on the need, team readiness and individual arrangement; it does not promise an immediate vacancy or start date.
Where does this work take place at Lucy Labs?
Lucy Labs confirms the working arrangement, employer, location, eligibility, compensation and terms for each role. Our mailing address does not determine the job location or guarantee remote employment, worldwide eligibility or visa sponsorship.
What does this role own at Lucy Labs?
Lucy Labs: Build the data paths that let Lucy understand work without losing where its evidence came from or how it must be protected. Your scope connects desktop observations to downstream processing and keeps public tools-and-techniques knowledge flowing into the Knowledge Graph. Reliable transport, storage and lifecycle make both paths usable and accountable.
How do I express interest in Lucy Labs?
Email people@lucylabs.ai with the role title, a short introduction and relevant CV or portfolio. Lucy Labs uses the application to assess and respond to it; retention for future opportunities is a separate explained purpose and choice.
