Senior Data Platform Engineer — Agentic Analytics (d/f/m

Description

mobile.de is Germany’s largest vehicle marketplace, with more than 1.6 million listed cars, commercial vehicles, motorcycles, and e-bikes (annual average 2025). Both private customers and registered vehicle dealers use the platform and benefit from more than 140 million visits per month*. As a “one-stop shop,” mobile.de’s offering includes not only buying and selling, but also financing and leasing solutions. Founded in 1996, the company is a subsidiary of Adevinta, a global leader in online classified portals.

Based in Berlin-Charlottenburg, we offer a dynamic environment where growth, learning, and collaboration are at the heart of everything we do.If you are driven by an eagerness to disrupt, have a passion for collaboration, and are excited about shaping the future of mobility, we would love to hear from you.

About the role

mobile.de's Data Platform team sits between raw data and every serious consumer of it: analysts, data scientists, product teams, and — increasingly — AI agents. We are building the data foundations that make agentic AI at scale trustworthy, governed, and fast.

We're looking for a Senior Data Platform Engineer who brings deep literacy and hands-on experience in agentic AI systems, a clear vision for analytical AI, and the engineering skills to turn that vision into production-grade platform capabilities. This role is not about maintaining data pipelines. It's about solving a problem the industry is still figuring out: how do you let an AI agent answer analytical questions against governed data and prove the answer is correct?

As a team, we believe our best work happens when we share context early, write decisions down, communicate clearly, and challenge ideas respectfully. We’re building a team where reliability is shared, learning and curiosity are encouraged, and different perspectives and working styles are welcome—so everyone can do their best work (and enjoy the ride while we’re at it).

Responsibilities

• Build agent-ready data infrastructure: Design and deliver the governance layer, semantic context, correctness benchmarks, and hardened tool integrations (e.g., MCP servers) that allow AI agents to access analytical data safely and verifiably.
• Own correctness for agentic analytics: Develop evaluation and validation approaches that improve the accuracy, reliability, and trustworthiness of agent-generated answers, bridging the gap between flexible AI systems and deterministic expectations.
• Strengthen our self-service data platform: Evolve our data mesh practices by building golden-path solutions and reusable blueprints that let domain teams ship data-driven capabilities quickly and independently — without handing long-term ownership back to the platform team.
• Partner across the organization: Work with Analytics, AI, Product, and Data Science stakeholders to identify needs, design solutions, and deliver platform capabilities that make their work easier and safer.
• Drive operational excellence: Integrate monitoring, alerting, FinOps, and observability into all platform components.
• Raise the bar: Contribute to architecture reviews, codify best practices into reusable playbooks, and help the next AI-data use case onboard dramatically faster than the first.

Requirements

• Proven builder of agentic AI systems: You have built or significantly contributed to at least one production system involving LLMs, AI agents, semantic layers, or tool orchestration (e.g., MCP, Skill building, RAG). AI isn't a buzzword on your CV — it's where you spend your engineering energy.
• Deep understanding of analytical AI challenges: You have hands-on experience in context engineering for agent-driven analytics — from grounding agent outputs in governed data, to building semantic layers, designing evaluation harnesses, and curating LLM-ready datasets. You can translate that into a practical strategy for making agentic analytics work reliably at platform scale — and deliver on it.
• End-to-end ownership: You take ambiguous, high-impact problems, engage the right stakeholders, design the solution, build it, and ship it — without waiting for detailed specifications.
• Senior engineering maturity: Multiple years of experience building scalable, reliable data or platform systems. Strong in Python or a JVM language, comfortable with SQL.
• Cloud platform fluency: Hands-on experience with a major cloud provider (we use GCP: BigQuery, Dataproc, Composer, IAM).
• Infrastructure & operations: