Senior Data & Platform EngineerGatekeeper Systems | Foothill Ranch, CA | On-site / Hybrid
THE OPPORTUNITYThis 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.
WHAT YOU WILL OWNRelational 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 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
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, 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
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 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
Customer and Operations Support- 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
Offshore Engineering and Product Team Collaboration- 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
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 - 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
WHAT WE ARE LOOKING FORMust-Have Experience- 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
Strong Preference- 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
Nice to Have- 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
