Director/Principal Engineer, Data & AI Platform

Arlo Technologies•Milpitas, CA

2d•$225,000 - $300,000

About The Position

Arlo is seeking a senior technical leader to transform its data platform into an AI-native internal product. The goal is to enable any team at Arlo to find trusted metrics and datasets without assistance, build new data products on shared primitives, and replace existing dashboards with governed, self-service versions. This role will bridge the gap between having data and acting on it by architecting and building systems that ensure data is accessible, reliable, and secure for various consumers, including AI tools and agents. This is primarily a software engineering and architecture role, requiring the candidate to write and ship production code, own testing and CI, and operate the systems they build. The role involves working horizontally across Data Engineering, Analytics, Cloud Infrastructure, and Enterprise Applications, serving as the technical point of contact for data modeling, cataloging, and exposure. The successful candidate will lead cross-team technical initiatives and focus on delivering running systems within the first six months, rather than solely producing documentation.

Requirements

Production experience building with LLMs, including retrieval and natural-language interfaces over structured data, context and prompt design, and evaluation, guardrail, and cost controls.
Shipped and operated LLM-based systems, not just prototypes.
Experience standing up ML as a platform capability and making it usable by non-ML engineers, including feature definition, training and serving paths, evaluation, and drift/quality monitoring.
Deep production experience with Lakehouse technologies like Unity Catalog, Delta Lake, and medallion architecture, including built-in metadata, lineage, and governance.
Production experience on Databricks or a comparable platform.
Production experience integrating heterogeneous data sources with different consistency models, key spaces, and access controls, including identity resolution and referential integrity without shared keys.
Experience designing query patterns that protect operational systems from analytical and agent workloads.
Experience with enterprise data architecture, including dimensional and semantic modeling, master data and ownership models, data contracts, and taxonomy design across multiple business domains and stacks.
Experience with AWS data services (DynamoDB, Redshift, Glue, or equivalents) in production.
Familiarity with Oracle EBS, Amplitude, Klaviyo, or similar enterprise and product-analytics sources is a strong plus.
Experience with access control and audit design for non-human consumers, and familiarity with MCP or comparable tool-callable interfaces.
10+ years in data engineering or data platform roles, with production ownership of systems depended upon by multiple teams.
Strong SQL and Python (or another general-purpose language) skills.
Experience with testing, code review, and production ownership for systems depended upon by other teams.
A track record of getting platform capabilities adopted by teams that do not report to you.

Nice To Haves

Experience with data catalog and governance tooling (Collibra, Atlan, or similar).
Experience in a regulated or compliance-sensitive data environment.
Experience replacing dashboard and reporting sprawl with governed self-service solutions.

Responsibilities

Treat the data platform as a product, understanding its consumers (analysts, engineers, applications, AI tools) and identifying areas for improvement.
Drive a roadmap focused on adoption, trust, and time-to-answer, and justify foundational investments with evidence of consumer needs.
Ship shared primitives that other teams can build upon, avoiding one-off deliverables or diverging starter kits.
Develop governed ML and AI features for analysts, such as anomaly detection, forecasting, segmentation, and natural-language query against the semantic layer.
Build repeatable AI capabilities with built-in evaluation, monitoring, drift, and cost visibility from the outset.
Design how AI tools and agents access the platform securely and reliably, ensuring they adhere to the same query-safety and permission guarantees as other consumers.
Solve the challenge of integrating data from heterogeneous sources (DynamoDB, Databricks, Oracle EBS, Amplitude, Klaviyo) by designing identity resolution, referential integrity, and consistency semantics.
Define query-safe access patterns to protect operational stores and ensure accurate data retrieval.
Develop a permission model that works across systems with different native access controls.
Design Arlo’s semantic and metric layer, along with the catalog, to ensure consistency, discoverability, and reusability of business terms, metric definitions, and ownership.
Define data product contracts, including schemas, ownership, freshness and quality SLAs, and access patterns, specifying what is safe to expose to different consumers.
Build metadata, lineage, and access-control models into the platform to automate governance.
Define and make visible what constitutes a 'trusted' data product at Arlo.
Partner with Data Engineering, Analytics, and AI engineering leadership on build-vs-buy decisions and project sequencing.