GCP Data Engineer

Design and develop scalable, high-performance backend services and APIs that process and expose telematics data, including:GPSTrip eventsDriver behaviorVehicle diagnosticsSensor telemetryBuild and maintain real-time and batch data pipelines that ingest high-volume vehicle event streams from messaging systems such as Pub/Sub and Kafka into data warehouses and operational stores.

GCP, Big Data, BigQuery, Artificial Intelligence & Expert Systems, API

Skills Preferred

• Google Cloud Platform
• Familiarity with advanced GCP services beyond core compute and storage, such as Vertex AI, Dataflow, Cloud Composer / Airflow, BigQuery ML
• Using Cloud Composer to orchestrate scheduled data pipelines that feed into a BigQuery data warehouse. Experience Required 7+ years of experience in IT, 5+ years of experience in software developmentGoogle Cloud Platform (GCP)Experience deploying and managing services on Google Cloud Platform, including Compute Engine, Cloud Storage, IAM, and Cloud Functions.Example: Designing and implementing a cloud-native application architecture using GKE with Cloud SQL and Pub/Sub.Big DataExperience working with large-scale data processing frameworks such as Apache Spark, Dataflow, or BigQuery.Example: Building ETL pipelines that process terabytes of daily event data and transform it for downstream analytics.Data WarehousingExperience designing and maintaining data warehouse solutions such as BigQuery, Snowflake, or Redshift.Example: Modeling a star schema for a retail analytics platform that supports reporting on sales, inventory, and customer behavior.Artificial Intelligence and Expert SystemsExperience developing or integrating AI/ML models and rule-based expert systems.Example: Building a classification model using Vertex AI to predict customer churn or implementing a rule engine that automates underwriting decisions.API DevelopmentExperience designing, building, and consuming RESTful or gRPC APIs.Example: Developing a versioned REST API with OAuth 2.0 authentication that serves as the integration layer between a mobile application and backend microservices. Preferred Experience Languages: Kotlin, Java, Python, or equivalent JVM/backend languageFrameworks: Spring Boot, gRPC, REST API designData Technologies: BigQuery, PostgreSQL, Redis, Bigtable, Kafka, or Pub/SubInfrastructure: GCP or equivalent, Docker, Kubernetes, CI/CD pipelinesPractices: TDD, MLOps-adjacent data pipeline patterns, database performance tuning, and API versioning, Bachelor’s Degree, Certification Program