Sr. Data and Platform Engineer

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Active - Regular full-time Foothill Ranch, CA, US

9 days ago Requisition

Salary Range: $ To $ Annually

At Gatekeeper Systems, we’re revolutionizing retail loss prevention and customer safety through a powerful combination of physical deterrents and cutting‑edge technology—including AI, computer vision, and facial recognition. As a global leader with over 25 years of industry excellence and a growing, diverse team of 500 employees across offices in North America, Europe, Australia, and Asia , we’re driven by innovation, integrity, and impact.

Join us and be part of a mission‑focused team that’s making a real difference in the future of retail, providing innovative solutions and services that redefine industry standards.

This is a senior hands‑on engineering role at the center of our data platform transformation. You will own the backend data infrastructure — relational database design, cloud data warehouse architecture, and the API layer that connects them to customer‑facing products and internal analytics tools.

This is not a pure architecture role and not a pure maintenance role. You will design systems and build them. You will mentor developers and review their code. You will triage a customer issue in the morning and design a new data schema in the afternoon. The role rewards engineers who thrive across the full stack of backend data work — from database DDL to API design to cloud infrastructure — and who move between strategic thinking and hands‑on execution without friction.

• Own the operational PostgreSQL database end‑to‑end: schema design, migration tracking, indexing strategy, connection pooling, high availability configuration, point‑in‑time recovery, and read replica management on Google Cloud SQL
• Design and maintain the data models that power product features, customer reporting, device management, alert processing, and LP intelligence workflows
• Build the new intelligence registry layer — persistent identity records, organizational grouping structures, asset tracking tables, and per‑location risk profiles — that enable cross‑incident and cross‑location analytics for the first time
• Enforce multi‑tenant data isolation: row‑level security at the database layer, strict per‑tenant query scoping enforced independently of application code

• Design and build a clean, layered Big Query data warehouse architecture — replacing a fragmented multi‑dataset structure accumulated without a canonical data model — organized into raw ingestion, curated analytics, and pre‑aggregated intelligence layers
• Build and maintain pre‑computed analytical views covering cross‑location activity patterns, organized retail crime group intelligence, regional trend heatmaps, travel pattern detection, and merchandise theft analytics — enabling LP investigators and directors to operate proactively rather than reactively
• Own data freshness, quality, and pipeline reliability across all layers — change data capture from the operational database, event stream subscriptions, and scheduled refresh jobs
• Design and implement a GKS‑owned cross‑retailer anonymized benchmark dataset — aggregating intervention outcomes and performance metrics across all deployments by store archetype, with strict retailer data separation — the data asset that enables GKS to show any customer how they compare to similar deployments across the network
• Manage Big Query cost and performance: partition and cluster strategy, BI Engine reservations, partition filter enforcement, materialized view design

• Design, build, and maintain the API layer that customer applications, internal analytics tools, and LP workflow platforms read from — GraphQL and REST, with performance, security, and scalability owned here
• Implement and maintain the versioned data contract between the…