Director/Principal Engineer, Data & AI Platform - Arlo Technologies

OpenTalent; Milpitas (CA); On-site; USD 140,000 - 180,000; Full time

Job summary

OpenTalent in Milpitas, CA, seeks a software engineering and architecture lead who writes and ships production code, owns tests and CI, and operates what you build with reliability guarantees. You will work across Data Engineering, Analytics, Cloud Infrastructure, and Enterprise Applications as the technical reference point for data modeling, cataloging, and exposure, while leading cross-team initiatives.

This role focuses on reusable primitives, data contracts, and a governed platform for

Qualifications

• Production software development experience desirable.
• Experience designing data platforms or large-scale data pipelines is a plus.

Responsibilities

• Write and ship production software.
• Own tests and CI around your code and operate what you build.
• Design interfaces, libraries, and primitives that other teams consume with strong versioning and reliability guarantees.
• Lead cross-team technical initiatives across Data Engineering, Analytics, Cloud Infrastructure, and Enterprise Applications.
• Define data product contracts, schemas, ownership, and quality SLAs to ensure trust and reusability.

Job description

This is a software engineering and architecture role first. You’ll write and ship production code, own the tests and CI around it, and operate what you build — designing the interfaces, libraries, and primitives other teams build on, with the versioning, backward-compatibility, and reliability guarantees that implies. Your first six months should produce running systems, not documents. If your recent work has been mostly diagrams, roadmaps, and specs, this isn’t the right fit.

You’ll work horizontally across Data Engineering, Analytics, Cloud Infrastructure, and Enterprise Applications as the technical reference point for how teams model, catalog, and expose data, and you’ll regularly lead cross-team technical initiatives.

What You’ll Own

Treat the platform as a product. Know your consumers — analysts, engineers, applications, AI tools — and where today’s experience falls short. Drive a roadmap measured on adoption, trust, and time-to-answer, and make the case for foundational investment with evidence of consumer need. Ship the shared primitives other teams build on top of — not one-off deliverables, and not starter kits that fork and diverge.

ML and AI as a capability . Ship the first governed features analysts can apply to trusted data products themselves: anomaly detection over metrics, forecasting, segmentation, natural-language query against the semantic layer. Build them as repeatable capabilities with evaluation, monitoring, drift and cost visibility from day one — not a portfolio of bespoke models. Design how AI tools and agents reach the platform (MCP or comparable) as a core interface held to the same query-safety and permission guarantees as any other consumer — not a side channel around the controls.

Solve the cross-store problem. Arlo’s data lives in systems that were never designed to be joined — DynamoDB for operational and device data, Databricks for analytics, plus Oracle EBS, Amplitude, and Klaviyo . Design the identity resolution, referential integrity, and consistency semantics that let a consumer trust a join across them. Define query-safe access patterns so a dashboard, application, or agent can’t overwhelm an operational store or quietly return a wrong answer — and a permission model that holds across systems with entirely different native access controls.

Architecture. On that foundation, design Arlo’s semantic and metric layer and the catalog, so business terms, metric definitions, and ownership are consistent, discoverable, and reusable. Define data product contracts — schemas, ownership, freshness and quality SLAs, access patterns — including what’s safe to expose to dashboards, applications, and AI tools.