Gatekeeper Security Jobs (NOW HIRING)

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

9 days ago Requisition ID: 1334

Salary Range: $110,000.00 To $145,000.00 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 BigQuery 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 BigQuery 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 operational database layer and the LP case management platform built by our partner engineering team — ensuring schema changes on either side are governed, tested, and do not produce silent breakage
• Work with the hardware and firmware engineering teams on the event publication pipeline — device pushout events, task queue publication to downstream services, fan‑out architecture for multi‑consumer event streams
• Design API access control — which user roles can access which data, how tenant identity is enforced end‑to‑end from authentication token through API to database row‑level security

• Own GCP data infrastructure as code using Terraform: managed database instances, data warehouse datasets, messaging topics, change data capture streams, serverless compute jobs, IAM bindings, VPC configuration, and secrets management
• Build and maintain CI/CD pipelines for data platform changes — migration gates, schema validation, deployment promotion through dev, staging, and production environments with automated quality checks
• GCP security posture: migrate all credentials to Secret Manager, enforce VPC Service Controls, apply per‑service least‑privilege access, enable audit logging, and build the evidence base needed for SOC 2 compliance
• GCP cost management across compute, storage, and analytics workloads

• Serve as the technical escalation point for data platform issues in production — work with the Operations team on customer triages, root cause analysis, and durable fixes that reduce recurring operational load
• Support the Operations team on BI reporting — help non‑engineering team members understand data structures, review and improve analytical queries, and build self‑serve analytics foundations that reduce engineering dependency
• Proactively identify when a schema change, pipeline delay, or performance issue will affect customer‑facing products — and surface it before it becomes a support ticket

• Work with the offshore engineering team on product data requirements — provide technical direction, code review, and mentoring across time zones for data layer integration work on our next‑generation experience platform
• Define the API surface and data models that the experience platform personas consume — ensure access control enforcement at the API layer aligns with data isolation enforcement at the database layer
• Collaborate with the partner engineering team on LP case management platform integration — boundary contracts, data contracts, versioning, and change governance

• Mentor junior developers on the data platform team — code review, architecture guidance, debugging technique, and cloud platform best practices
• Be the technical anchor for the offshore engineering team on data platform work — design direction, implementation unblocking, and asynchronous work review
• Guide the Operations team on data literacy — help them understand data structure well enough to build and interpret business reports without engineering involvement for routine requests
• Leverage Claude AI and other AI coding tools as a productivity standard — not as a pilot but as the baseline expectation for design, research, code generation, and documentation. Model this for the team

• 7+ years of hands‑on backend data engineering with clear ownership of production systems — not advisory or architecture‑only roles
• Deep PostgreSQL expertise: schema design, query optimization, indexing, migration management, connection pooling, and managed cloud database operations including high availability and point‑in‑time recovery
• BigQuery mastery: partitioning and clustering strategy, materialized views, BI Engine, authorized views, row‑level security, change data capture integration, and cost control through physical design and slot reservations
• Python as your primary language — production‑quality Python on serverless compute, event‑driven functions, and GCP SDK integrations, not just scripting
• GCP platform depth across managed database, analytics warehouse, object storage, messaging, serverless compute, secrets management, IAM, VPC, and infrastructure‑as‑code with Terraform — you have built and operated GCP infrastructure, not just consumed managed services
• API design: you have designed and maintained REST or GraphQL APIs that production applications depend on, with authentication, role‑based access control, and performance characteristics appropriate for a multi‑tenant platform
• Security by default: credentials in secrets management, row‑level security at the database layer, least‑privilege service accounts, audit logging — these are your starting point, not a checklist to fill in later
• Multi‑tenancy architecture: you have built systems where strict per‑tenant data isolation is a hard constraint enforced at multiple independent layers simultaneously
• AI‑native productivity: you use Claude AI or equivalent tools daily for design research, code generation, documentation, and debugging. Your output is measurably higher than a developer who does not. This is required, not preferred

• GCP DevOps and infrastructure experience: Terraform, Cloud Build or GitHub Actions, deployment promotion across environments with automated quality gates
• Experience working alongside embedded firmware or hardware teams — understanding how device telemetry is generated, what edge event streams look like, and how to debug issues that span hardware and cloud software
• Hardware and software integration testing: you can test a system that involves physical devices in combination with the backend services they talk to
• Experience on a team with offshore or contractor components — you know how to provide technical direction asynchronously, structure work for distribution, and maintain quality across time zones
• Dimensional data modeling: you understand fact and dimension schema design and know when to apply it versus when a simpler approach is more appropriate
• Domain knowledge in retail technology, IoT, physical security, or loss prevention

• GoLang familiarity — the device event pipeline is written in Go and you will occasionally need to read and contribute to it
• Real‑time OLAP database experience (ClickHouse, Apache Pinot, or similar) — will inform future architecture decisions as analytical data volumes scale
• dbt or Dataform experience for transformation layer definition and lineage
• ML pipeline or MLOps exposure — the active learning pipeline for device classification intersects with the data platform

This Job Description is a general overview of the requirements for the position. It is not designed to contain, nor should it be interpreted as being all inclusive of every task which may be assigned or required. It is subject to change, in alignment with company/department needs and priorities.

Gatekeeper Systems, Inc., is an equal opportunity employer. We are committed to developing a diverse workforce and cultivating an inclusive environment. We value diversity and believe that we are strengthened by the differences in our experiences, thinking, culture, and background. We strongly encourage applications from candidates who demonstrate that they can contribute to this goal. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status or any protected basis.