Sr. Data and Platform Engineer

About the Role

Gatekeeper Systems is a PE-backed retail loss prevention technology company serving major global retailers. We operate smart cart containment and facial recognition identity platforms deployed in thousands of retail locations worldwide. We are in the most consequential transition in the company's history — evolving from a hardware company into a loss prevention operating system and data intelligence platform. Our data platform underpins that transition.

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. 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.

Responsibilities

• Relational Database — Design, Operations, and Reliability
  • 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
• Cloud Data Warehouse — Architecture and Analytics
  • Design and build a clean, layered BigQuery data warehouse architecture — replacing a fragmented multi-dataset structure — 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
  • 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 with strict retailer data separation
  • Manage BigQuery cost and performance: partition and cluster strategy, BI Engine reservations, partition filter enforcement, materialized view design
• API Design and Backend Engineering
  • Design, build, and maintain the API layer that customer applications, internal analytics tools, and LP workflow platforms read from — GraphQL and REST
  • Implement and maintain the versioned data contract between the operational database layer and the LP case management platform
  • Work with hardware and firmware engineering teams on the event publication pipeline
  • Design API access control — role-based data access and tenant identity enforcement
• GCP Infrastructure and DevOps
  • 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 environments
  • GCP security posture: migrate credentials to Secret Manager, enforce VPC Service Controls, apply least-privilege access, enable audit logging for SOC 2 compliance
  • GCP cost management across compute, storage, and analytics workloads
• Customer and Operations Support
  • Serve as the technical escalation point for data platform issues in production
  • Support the Operations team on BI reporting — help non-engineering team members understand data structures and build self-serve analytics foundations
  • Proactively identify when schema changes, pipeline delays, or performance issues will affect customer-facing products
• Offshore Engineering and Product Team Collaboration
  • Work with the offshore engineering team on product data requirements — provide technical direction, code review, and mentoring
  • Define the API surface and data models that the experience platform consumes
  • Collaborate with the partner engineering team on LP case management platform integration
• Mentoring and Technical Leadership
  • 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
  • Guide the Operations team on data literacy
  • Leverage Claude AI and other AI coding tools as a productivity standard for the team

Requirements

• 7+ years of hands-on backend data engineering with clear ownership of production systems
• 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
• Python as your primary language — production-quality Python on serverless compute, event-driven functions, and GCP SDK integrations
• GCP platform depth across managed database, analytics warehouse, object storage, messaging, serverless compute, secrets management, IAM, VPC, and infrastructure-as-code with Terraform
• API design experience: REST or GraphQL APIs with authentication, role-based access control, and performance characteristics for multi-tenant platforms
• Security by default: credentials in secrets management, row-level security at the database layer, least-privilege service accounts, audit logging
• Multi-tenancy architecture: strict per-tenant data isolation enforced at multiple independent layers
• AI-native productivity: daily use of Claude AI or equivalent tools for design research, code generation, documentation, and debugging

Preferred Qualifications

• GCP DevOps and infrastructure experience: Terraform, Cloud Build or GitHub Actions, deployment promotion across environments
• Experience working alongside embedded firmware or hardware teams
• Hardware and software integration testing experience
• Experience on a team with offshore or contractor components
• Dimensional data modeling expertise
• Domain knowledge in retail technology, IoT, physical security, or loss prevention
• GoLang familiarity for device event pipeline contributions
• Real-time OLAP database experience (ClickHouse, Apache Pinot, or similar)
• dbt or Dataform experience for transformation layer definition
• ML pipeline or MLOps exposure