Back End SW Development Engineer

Google Cloud Software Engineer – Dearborn, Michigan

Position is based at Dearborn, Michigan. This role offers an opportunity to work in a corporate environment and advance your career.

• GCP – Experience deploying and managing services on Google Cloud Platform, including Compute Engine, Cloud Storage, IAM, and Cloud Functions. For example, designing and implementing a cloud-native application architecture using GKE (Google Kubernetes Engine) with Cloud SQL and Pub/Sub.
• Big Data – Experience working with large-scale data processing frameworks such as Apache Spark, Dataflow, or Big Query. For example, building ETL pipelines that process terabytes of daily event data and transform it for downstream analytics.
• Artificial Intelligence & Expert Systems – Experience developing or integrating AI/ML models and rule-based expert systems. For example, building a classification model using Vertex AI to predict customer churn, or implementing a rule engine that automates underwriting decisions.
• API – Experience designing, building, and consuming RESTful or gRPC APIs. For example, developing a versioned REST API with OAuth 2.0 authentication that serves as the integration layer between a mobile application and backend microservices.

• Architect and optimize data access layers across heterogeneous storage systems — including relational databases (PostgreSQL, Cloud SQL), columnar warehouses (Big Query), in-memory caches (Redis), and wide-column stores (Bigtable) — selecting the appropriate store for each access pattern.
• Collaborate with data engineers and analysts to design stored procedures, views, and query patterns in analytical databases that meet strict latency and throughput SLAs for reporting endpoints.
• Implement data aggregation, transformation, and enrichment logic — including time-series rollups, geo-spatial calculations, and unit/timezone conversions — to produce accurate, consistent reporting outputs.
• Build and enforce data contracts and schema evolution strategies to ensure backward compatibility and stability across upstream producers and downstream API consumers.
• Integrate backend services with cloud-native infrastructure (GCP, AWS, or Azure) including event-driven architectures, scheduled jobs, serverless functions, and container orchestration platforms (Kubernetes/GKE).
• Instrument services with observability tooling — structured logging, distributed tracing, and metrics — and participate in on-call rotations to maintain high availability and reliability targets (SLO/SLA).
• Apply security best practices including authorization scoping (e.g., segment- or fleet-scoped data access), secrets management, and data privacy controls in compliance with automotive and enterprise data regulations.
• Partner with product, data science, and platform teams in an agile delivery model — contributing to technical design reviews, code reviews, and architectural decisions for new capabilities on the telematics platform.