Lead Data Platform Engineer (German Speaker) - ScraperAPI

This role is part of our INFOnline team, one of our exciting brands at saas.group. INFOnline powers digital audience measurement for the German and Austrian media industry, processing billions of events to deliver trusted reach and engagement metrics.

As part of saas.group, we have modernized our business-critical infrastructure and moved towards a fully cloud-native architecture on GCP. With the major migration work complete, we are looking for a strong technical owner to run, harden, scale, and evolve the new platform.

Profile Overview

We’re looking for a technically deep, hands-on Lead Data Platform Engineer to take full ownership of INFOnline’s central data platform from raw event ingress through processing, aggregation, data modeling, and reporting delivery.

This is not a role where you simply follow someone else’s roadmap. You will help define how the platform should mature: where we need stronger observability, better data quality controls, clearer ownership boundaries, improved documentation, cost efficiency, and more scalable operating models.

This is a hands-on technical leadership role. You will set technical direction, make architecture decisions, establish engineering standards, mentor others, and work close to the code and systems. You will also help drive AI-native engineering practices using coding agents, AI-assisted testing, documentation, refactoring, and incident analysis to increase engineering speed and quality.

Immediate Impact (First 3–6 Months)

• Audit and map the existing cloud and data platform architecture to identify critical risks, dependencies, and improvement opportunities.
• Take ownership of core platform components from data ingress to reporting, supported by structured knowledge transfer.
• Establish full internal ownership of the new cloud-native data platform.
• Improve data quality controls, validation processes, and operational safeguards.
• Build a pragmatic post-migration roadmap focused on stability, scalability, data quality, cost efficiency, and business continuity.
• Strengthen monitoring, alerting, and observability for business-critical data workflows and delivery pipelines.
• Establish engineering standards around documentation, code reviews, testing, and AI-native development practices.

Responsibilities

• Own the end-to-end data platform roadmap and drive its execution from architecture decisions to day-to-day platform operations.
• Take accountability for data ingress, streaming processing, batch aggregation, data modeling, quality, delivery, and reporting logic.
• Ensure reliability, scalability, and performance through strong monitoring, observability, and incident management practices.
• Continuously improve the GCP-native platform with a focus on stability, cost efficiency, maintainability, and business continuity.
• Collaborate closely with Product, Customer Success, and Leadership to translate business requirements into scalable technical solutions.
• Drive AI-native engineering adoption (AI-assisted coding, refactoring, testing, documentation) and establish standards for safe, effective use.
• Work with external specialists where useful and establish sustainable internal ownership of all critical platform components.

What You Bring to the Table

• 5+ years of experience in Data Engineering, Data Platform Engineering, or Platform Engineering in production environments.
• Solid Go (Golang) proficiency is required (core backend systems, ingress collectors, Pub/Sub processors, Dataflow jobs, batch loaders, CLI tools).
• Strong hands-on experience with GCP Cloud Run, Pub/Sub, BigQuery, Dataflow, Cloud Storage, and Cloud SQL.
• Strong SQL and analytical data modeling skills; familiarity with SQLMesh or similar dbt-style orchestration tools is a significant plus.
• Practical experience with Terraform / IaC, CI/CD pipelines, and containerized workloads (Docker, Cloud Run-style serverless).
• Experience with Protobuf or comparable schema definition and serialization frameworks.
• Familiarity with IVW, OEWA, or comparable digital audience measurement standards (or a genuine interest in the web analytics domain).
• Experience with AI coding assistants and coding agents (Claude Code, Codex, etc.) with sound judgment in reviewing AI-generated code.
• Excellent communication skills with both technical and non-technical stakeholders.
• Fluent German (C1) required; good English for technical documentation and saas.group collaboration.

What’s in it for You

• Ultimate flexibility: 100% remote work from wherever you like, whenever you like.
• Freedom and autonomy: High-trust team environment with flexibility to solve problems your own way.
• Minimum bureaucracy: Efficient processes with minimal red tape and meetings.
• Small & friendly team: Collaborative, fun, and supportive engineering culture.
• Our network: Access to a community of entrepreneurial SaaS professionals for idea and knowledge exchange.