About this role
We are seeking an expert in modern data architecture and cloud solutions to develop cloud-based data platform solution accelerators for high-priority use cases, with a strong focus on connected mobility and fleet management. The role focuses on building reusable, production-grade codebases that can be rapidly adapted for customer pilots, accelerating both sales cycles and implementation efforts.
Location: Berlin
Work Schedule: 40 hours per week / Fully remote
Key Responsibilities
Solution Development:
Act as a hands-on developer to build production-ready solution accelerators with reusable, modular, and adaptable codebases.Performance Optimization:
Design and implement low-latency data pipelines, including the transition from micro-batch processing to streaming architectures using SQL-based processing frameworks and native streaming ingestion services.Documentation & Assets:
Deliver enablement assets such as GitHub repositories, architecture diagrams, and technical documentation to support field teams and partners (e.g., system integrators) in implementations and customer pilots.
Requirements
Real-Time / Streaming Ingestion:
Hands-on experience with native streaming ingestion services for high-volume, low-latency telematics data.Application Hosting:
Experience using managed container services within a cloud data platform to containerize and deploy services, including cost-effective open-source routing engines.Visualization:
Strong expertise in building front-end applications and dashboards using modern data app frameworks (e.g., Streamlit) to deliver actionable operational insights (KPIs, alerts, heatmaps).Data Science & Machine Learning:
Working knowledge of applying machine learning models for analytical and predictive use cases on large-scale datasets.Advanced Analytics (Nice to Have):
Experience with time-series analytics and geospatial capabilities, including H3 hexagonal indexing.
Preferred Industry Experience
Experience with fleet and IoT intelligence use cases is a strong plus, including:
Predictive Maintenance:
Integrating IoT sensor data (e.g., diagnostic trouble codes, voltage, temperature) to predict asset failures and trigger automated work orders.Fleet Efficiency & Optimization:
Route optimization and deviation detection
Inbound logistics visibility for just-in-time production
Idling reduction using near real-time vehicle telemetry
IoT Operations Intelligence:
Building reusable architectures that address shared challenges across moving and stationary assets, such as time-to-action and predictive maintenance.