Senior Software Engineer (m/w/d)

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.