Spatial Data Jobs in Michigan (NOW HIRING)

Position Description:

Employees in this job function are responsible for designing, building, and operating high-throughput backend systems that ingest, process, and serve large volumes of telematics data from connected vehicle fleets. They work at the intersection of distributed systems, data engineering, and API development — delivering reliable, low-latency services that power fleet intelligence products for commercial and enterprise customers. Design and develop scalable, high-performance backend services and APIs that process and expose telematics data — including GPS, trip events, driver behavior, vehicle diagnostics, and sensor telemetry — at fleet scale Build and maintain real-time and batch data pipelines that ingest high-volume vehicle event streams from messaging systems (e.g., Pub/Sub, Kafka) into data warehouses and operational stores

Skills Required:

GCP, Big Data, Artificial Intelligence & Expert Systems, API 1. 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. 2. Big Data – Experience working with large-scale data processing frameworks such as Apache Spark, Dataflow, or BigQuery. For example, building ETL pipelines that process terabytes of daily event data and transform it for downstream analytics. 3. 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. 4. 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.

Skills Preferred:

Google Cloud Platform 1. Google Cloud Platform – Familiarity with advanced GCP services beyond core compute and storage, such as Vertex AI, Dataflow, Cloud Composer (Airflow), and BigQuery ML. For example, using Cloud Composer to orchestrate scheduled data pipelines that feed into a BigQuery data warehouse.

Experience Required:

Senior Engineer Exp: Prac. In 2 coding lang. or adv. Prac. in 1 lang.; guides. 7+ years in IT; 5+ years in development

Experience Preferred:

Languages: Kotlin, Java, Python, or equivalent JVM/backend language Frameworks: Spring Boot, gRPC, REST API design Data: BigQuery, PostgreSQL, Redis, Bigtable, Kafka or Pub/Sub Infrastructure: GCP (or equivalent), Docker, Kubernetes, CI/CD pipelines Practices: TDD, MLOps-adjacent data pipeline patterns, database performance tuning, API versioning

Education Required:

Bachelor's Degree

Education Preferred:

Certification Program

Additional Safety Training/Licensing/Personal Protection Requirements:

Additional Information :

***POSITION IS HYBRID / 3 - 4 DAYS PER WEEK IN THE OFFICE*** Architect and optimize data access layers across heterogeneous storage systems — including relational databases (PostgreSQL, Cloud SQL), columnar warehouses (BigQuery), 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