Senior Customer Support Engineer

Short Description

At SPREAD, we enable engineering teams at leading companies such as Mercedes-Benz, Volkswagen, BMW, Bosch, and Rheinmetall to navigate the complexities of software-defined vehicles and products . Our platform transforms intricate engineering and software data into actionable Engineering Intelligence, providing teams with preconfigured applications and AI agents to access and apply critical product knowledge more effectively. This approach accelerates time-to-market by up to 10× , r educes costs by 50% , and enhances product quality .
Recognized for our innovation with accolades like the 2024 SPARK AI Innovation Prize (by Handelsblatt & McKinsey), inclusion in the EU AI Champions Initiative , and ranking among LinkedIn’s Top 20 Startups in DACH , SPREAD offers a dynamic environment where your contributions directly impact the evolution of engineering intelligence.

Your Mission

• Own the helpdesk end-to-end, covering intake, triage, internal routing, and customer communication. Implement automations, SLA policies, forms, and runbooks, and publish weekly, monthly, and quarterly KPI reports.
• Reproduce and resolve, route or escalate issues across product features, ETL pipelines, GraphQL queries, database traversals, and SQL scripts. Capture and follow up, if not provided by a user/client, evidence such as screenshots, videos, logs, and traces, and deliver clear Root Cause Analyses.
• Work with Product and Engineering to file high-quality bug reports, verify fixes, and close the loop with customers.
• Establish and evolve support operations. Define SLA targets (based on MSA), escalation paths, incident communication templates, and internal ticket flows that scale with volume.
• (Re-)Categorize user requests to distinguish between different request types and corresponding SLA targets.
• Sync with Product and Engineering to gain a firm understanding of our products and tech stack to better reproduce/understand support requests and create CS function handbooks.
• Raise the bar on the helpdesk stack by refining queues, fields, and taxonomies, implementing automation rules, and connecting with status pages, analytics, and logging or telemetry where useful.
• Create and maintain knowledge base articles and runbooks with the Technical Writer to drive self-service and deflect tickets.
• Coach teammates on effective handoffs, and keep ticket hygiene high so reporting and trend analysis stay reliable.
• Watch for performance and scale risks in the knowledge graph, and partner with Engineering on pragmatic mitigations such as indexing guidance, caching strategies, and safeguards.
• Represent Support in customer communication and give clear and empathetic updates during investigations and after resolution.
• Track and report support and reliability KPIs to Product leadership and propose quarterly improvement initiatives based on trend data.

Your Experience

• Fluent German and English with confident customer-facing writing and speaking.
• 5+ years in enterprise technical support or a similar customer-facing role for complex B2B software with a track record of setting up processes and tooling.
• Hands-on experience with helpdesk tooling e.g. Jira Service Management, including SLAs queues, forms, automations, and dashboards, with comfort in defining escalation paths and reporting cadences.
• Proficiency in JavaScript and/or Python with the ability to write SQL scripts for diagnostics and data validation.
• Practical familiarity with ETL concepts, GraphQL, and databases, with the ability to isolate data flow and query issues.
• Ability to reason about SaaS on AWS, Azure, and GCP, as well as on-prem environments, with an understanding of configuration, identity, and network differences and their impact on reproducibility and fixes.
• Strong debugging skills across operating systems, networking, and web or application layers, with confidence in gathering logs and traces and structuring hypotheses.
• Understanding of the low-code application layer with the ability to diagnose configuration logic flow and schema interactions that influence behavior and performance.
• Familiarity with automotive machinery, defence aerospace, or similar machine-building domains, and understanding of how complex systems are built.
• A documentation habit with precise knowledge base articles, runbooks, and internal notes that accelerate future resolution.

Why SPREAD?

• Category Creators : Our Agentic Engineering Intelligence platform connects fragmented hardware and software data into dynamic Product Twins . AI agents operate on this foundation to automate compliance checks, dependency analysis, and spec generation — enabling 10× faster time-to-market and 50% lower lifecycle costs for OEMs like Volkswagen, Mercedes-Benz, and Rheinmetall. Agentic Engineering SPREAD's Engineering Tech
• Industry-Relevant Impact : Traditional PLM systems hit their limits with software-defined products. At SPREAD, you'll guide leading manufacturers into the AI-driven era of engineering — unlocking millions in savings. Engineering Intelligence
• High Growth & Ownership : We’ve grown double digits every year since 2019 — and there’s a greenfield ahead in untapped industries and regions, ready for you to shape.
• Competitive Package : Attractive compensation + performance bonus , mobility budget (e.g. Deutschlandticket), Urban Sports Club membership , 30 vacation days , workation options , a volunteering day , and a €1,000 learning budget .
• Security & Flexibility : ISO 27001 and TISAX certified, fully GDPR-compliant, with flexible deployments (cloud or on-prem) and a hybrid work model designed to fit your life.

Contact

Dmytro İefymenko
[email protected]

SPREAD AI
Köpenicker Str. 40c | 10179 Berlin

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About us

SPREAD is pioneering Agentic Engineering Intelligence . Our low-code platform transforms fragmented hardware and software product data into dynamic Product Twins , creating an AI-ready knowledge base that powers smarter decisions and automation across the entire product lifecycle.
At the heart of our technology lies the world’s first Universal Engineering Information Model (EIN) — a semantic framework that connects domain-specific and lifecycle data into a federated Engineering Intelligence Graph , continuously evolving over time. This foundation enables specialized AI agents to execute tasks like compliance checks, dependency analysis, and specification generation — seamlessly integrated into the tools engineers already use.
The impact is tangible: companies using SPREAD achieve up to 10x faster time-to-market , 50% lower lifecycle costs , and significantly higher product quality .
Industry leaders such as Volkswagen, Mercedes-Benz, BMW, Audi, Rheinmetall , and others rely on SPREAD to build the next generation of software-defined products.
Headquartered in Berlin, SPREAD delivers enterprise-grade security (ISO 27001 & TISAX), full GDPR compliance , and flexible deployment options — in the cloud or on-premises.

Our Vision
The future of engineering is data-driven and agentic . We make product data the most accessible, intuitive, and impactful resource in engineering — so that engineers can unleash their full creativity and drive innovation at scale.

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