Backend Software Engineer — Data Platform & AI Data Products

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

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401(k) match

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

30% above the BLS national median for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

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Assessment

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Deadline

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Where they hire

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About this role

Backend Software Engineer — Data Platform & AI Data Products San Francisco About the Role You'll join the Data Platform team, responsible for building the backend services and “data products” that power how data moves through the company. We create the core platform primitives - high-quality event streams, reliable access layers, and developer-friendly APIs/tools - so teams across the org can self-serve what they need and ship faster. You'll contribute to backend services that create value from our company data, and help make our data platform more self-serve so product and engineering teams can easily create and operate event-driven architectures, publish/consume streams, define access models, and ship data products end-to-end. You'll also work on LLM-adjacent services such as prompt categorization/taxonomy, enrichment, and metadata systems that turn raw telemetry into trusted, usable products - with mentorship and support from experienced engineers. Responsibilities - Contribute to backend services that enhance the data platform's capabilities (APIs, control planes, automation, governance). - Help enable DIY workflows for teams across the company: - - Define/publish events and schemas - Create/consume streams and subscriptions - Establish access models (authz, row/field-level controls where applicable) - Manage dataset/catalog metadata, lineage, versioning, and retention - Contribute to end-to-end data products: ingestion → validation/quality → enrichment → serving (APIs/streams) → observability → adoption. - Work on prompt categorization and enrichment services: taxonomy design, labeling workflows, classifier/rules integration, evaluation, drift/quality monitoring, and safe rollouts. - Learn to own reliability: SLOs, alerting, performance/cost tuning, incident response, and postmortems. - Partner cross-functionally with

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