Sr. Data and Platform Engineer

GATEKEEPER SYSTEMS•Lake Forest, CA

3d•$150,000 - $185,000•Hybrid

About The Position

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. It processes events, video metadata, device telemetry, and analytics from tens of thousands of deployed edge devices across customer retail sites, and it is growing significantly in scope and strategic importance. The person in this role will be central to how that platform evolves.

Requirements

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

Nice To Haves

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

Responsibilities

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