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 new 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, including AI-assisted coding, refactoring, testing, documentation and code reviews 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. Our core backend systems, including ingress collectors, Pub/Sub processors, Dataflow jobs, batch loaders, controllers and CLI tools are written in Go.
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, not cluster Kubernetes)
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 and the ability to ramp quickly
Experience with AI coding assistants and coding agents (Claude Code, Codex, or similar) and sound judgment in reviewing AI-generated code for quality, security, and operational risk
Excellent communication skills with both technical and non-technical stakeholders
Fluent German (C1) required; good English for technical documentation and saas.group collaboration
Benefits
Ultimate fle
Additional Information
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. Our systems process billions of events and deliver the trusted reach and engagement metrics used by publishers, advertisers, agencies, IVW and OEWA.
As part of saas.group, we have been modernizing our business-critical infrastructure and moving towards a fully cloud-native architecture on GCP. The major migration work is complete. Now 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.
You will take ownership of a newly built GCP-native data platform as it moves from completed migration into long-term production operation, optimization and continuous evolution.
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 still work close to the code and systems.
On top of that, you will help drive AI-native engineering practices using coding agents, AI-assisted testing, documentation, refactoring and incident analysis to increase engineering speed and quality.
Your immediate impact in the first 3-6 months will be:
Audit and map the existing cloud and data platform architecture identify critical risks, dependencies, and improvement opportunities
Take ownership of core platform components from data ingress to reporting, supported by structured knowledge transfer where needed
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 IVW/OEWA delivery pipelines
Establish engineering standards around documentation, code reviews, testing and AI-native development practices
Saas · Europe