Data Engineer

We are looking for a Data Integration Engineer to own the data pipelines and integration layer that powers our AI Search Platform. You will design, build, and maintain the workflows that move data reliably from source systems into GCP services - including Vertex AI - and expose that capability through secure, well-designed APIs consumed by internal and external systems. This is a hands-on engineering role. You will write production code, own the reliability of what you ship, and work closely with DevOps, Network, and Platform Engineering teams.

Tech you will work with daily: Python | SQL | GCP (Cloud Run, Pub/Sub, Cloud Storage, Cloud Spanner, Vertex AI) | Terraform | PostgreSQL | Docker | Git | CI/CD, Data Integration & Pipeline Development

• Design, implement, and optimise scalable data integration workflows supporting inference and data synchronisation across GCP services (Cloud Run, Pub/Sub, Cloud Storage, Cloud Spanner, Vertex AI)
• Build and maintain event-driven pipelines and ETL/ELT workflows that deliver clean, reliable data to the AI Search Platform
• Automate deployment, testing, and pipeline orchestration using Cloud Run, Pub/Sub triggers, and Terraform

API Development for AI Integration

• Build and maintain APIs that expose data integration and AI inference capabilities to internal and external systems
• Ensure secure, reliable, and performant access to the AI Search Platform - correct authentication, rate limiting, and error handling by default

Permissions & Compliance Layer

• Integrate and enforce API and IAM policies for compliant access control across all AI Search Platform components
• Own and evolve the permissions API layer to meet growing scalability and security requirements

Data Quality & Reliability

• Ensure data integrity through monitoring, validation, and alerting across all integrated systems and services
• Continuously monitor workflows for latency, reliability, and cost efficiency - implement improvements without waiting to be asked

Documentation & Standards

• Maintain architecture documentation and runbooks
• Contribute to best practices for data integration, reproducibility, scalability, and security, * Month 3: Core data pipelines understood and contributing to production; first reliability or latency improvement shipped
• Month 6: Owning at least one integration area end-to-end; permissions API layer extended with evidence-backed design decisions
• Month 12: Data integration reliability measurably improved; pipeline documentation and monitoring coverage complete; identified and closed at least one material cost or latency inefficiency

You're probably NOT a fit if

• Your data engineering experience is primarily batch ETL without event-driven or streaming context
• You are not comfortable working across cloud-native GCP services in production
• You treat IAM and access control as someone else's concern
• You need fully defined requirements before designing an integration

You have 3+ years of hands-on experience in data engineering, cloud integrations, or backend development and have shipped production data pipelines on GCP. Specifically:

• 3+ years of professional experience in data engineering, cloud integrations, or backend development

• Strong proficiency in Python and SQL

• Production experience with Google Cloud Platform services: Cloud Run, Pub/Sub, Cloud Storage,Cloud Spanner, and Vertex AI

• Experience with event-driven architectures and cloud-based ETL/ELT workflows

• Experience with relational databases (PostgreSQL, Cloud Spanner) and exposure to NoSQL

• Proficient with Git and familiar with CI/CD workflows and containerisation (Docker)

• Experience with Terraform or equivalent Infrastructure-as-Code tooling

• Working knowledge of IAM, data governance, and access management principles

Nice-to-Have (Bonus Skills)

• Azure DevOps or cross-cloud integration experience
• API design experience (REST or gRPC)
• Experience with AI/ML inference pipelines or Vertex AI in production
• Prior work in construction, engineering, or real estate software domains

Soft Skills

• Engineering rigour - you care about pipeline reliability and data correctness, not just throughput
• Ownership mindset - you monitor what you build and fix it when it breaks
• Clear written communication: able to document integration contracts and architecture decisions for non-specialist readers
• Collaborative: comfortable working across DevOps, Network, and Platform teams without friction
• Comfortable with ambiguity - you can scope and deliver integration work from incomplete upstream specs

• Competitive fixed salary - shared on request
• Variable performance bonus: 5% of fixed
• Continuous learning & certification budget Learning programmes | Career growth | International exposure

thinkproject was founded in 2000 in Munich, Germany. Since then, the company has grown into the leading provider for cross-enterprise collaboration and information management in Europe.

Global customers from the construction and engineering industries are served from thinkproject’s home base in Munich and via a range of subsidiaries across Europe.

thinkproject addresses today’s digitization challenges in construction and engineering by providing state-of-the-art software solutions as well as industry expert consulting and services.