This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Product Data Engineer based in Germany.
This is a senior data engineering opportunity focused on building customer-facing data products from complex, large-scale, and often unstructured data.
You will join a specialized Data Insights team responsible for turning raw signals into reliable, valuable intelligence that powers core product experiences.
Your work will span data processing, ETL/ELT, orchestration, AI-assisted search, LLM-powered capabilities, and scalable data architectures.
You will own high-impact projects end-to-end, from initial idea and architecture through production launch, iteration, and optimization.
The role combines hands-on engineering with strong product thinking, requiring you to balance quality, performance, cost, and customer value.
You will work in a remote-first environment with high autonomy, fast feedback loops, pair programming, and limited unnecessary meetings.
This is an opportunity to shape the data backbone behind innovative customer-facing products while helping define how AI and large-scale data processing are used in production.
Accountabilities:
You will take ownership of complex data products and systems, working across engineering, AI, and product capabilities to deliver reliable and scalable customer-facing solutions.
- Design, build, and operate data products that transform raw social and public data into consistent, customer-facing insights.
- Develop large-scale ETL/ELT pipelines and data processing systems using Spark, with PySpark as a preferred technology.
- Work with unstructured and complex datasets to extract meaningful information and create reliable data products.
- Own projects end-to-end, from discovery, planning, and scoping through architecture, implementation, production release, and iteration.
- Build and improve systems that generate insights such as creator locations, demographics, interests, brand collaborations, and other data-driven intelligence.
- Contribute to the development of AI-assisted search, recommendations, and other intelligent product capabilities using LLMs and embeddings.
- Build and operate LLM-powered and agentic features in production environments.
- Design reliable workflows and orchestration processes using tools such as Airflow or AWS Step Functions.
- Work across AWS and GCP infrastructure to support scalable data processing, storage, and AI workloads.
- Monitor system performance, reliability, data quality, and operational costs as data volumes and product usage grow.
- Make informed trade-offs between LLM capability, latency, reliability, and cost.
- Collaborate with data engineers, backend engineers, and other technical stakeholders through pair programming, code reviews, and rapid feedback cycles.
- Contribute to system architecture and technical decisions while maintaining high standards for code quality, scalability, and maintainability.
- Help evolve data systems and customer-facing capabilities as product requirements and technologies change.
Requirements:
The ideal candidate is a hands-on senior data engineer who combines strong large-scale data processing expertise with product ownership, modern AI capabilities, and a pragmatic approach to system design.
- Strong professional knowledge of Apache Spark, with PySpark preferred; experience with Scala or Databricks is also valuable.
- Proven experience building ETL/ELT pipelines and processing data at significant scale.
- Comfortable working with unstructured, messy, and complex datasets.
- Hands-on experience with workflow orchestration tools such as Airflow or AWS Step Functions.
- Familiarity with the AWS ecosystem, particularly services such as Glue and EMR.
- Demonstrated ability to ship complete production features from idea and scoping through architecture, implementation, release, and iteration.
- Hands-on experience building and deploying agentic or LLM-powered features in production.
- Practical understanding of LLM trade-offs involving cost, latency, performance, and capability.
- Strong system design and software engineering fundamentals.
- High attention to code quality, reliability, scalability, and maintainability.
- Experience working autonomously and taking ownership of complex technical problems.
- Strong communication skills and ability to provide direct, constructive feedback within a collaborative engineering environment.
- Based in Europe with significant working-hours overlap with EET/Tallinn time.
- Experience with AI/ML tools and LLM technologies is a plus.
- Familiarity with GCP, particularly Vertex AI, is advantageous.
- Experience with lakehouse technologies such as Apache Iceberg is beneficial.
- Experience using Pulumi or Terraform for infrastructure as code is a plus.
- Familiarity with Node.js and TypeScript is advantageous.
- Understanding of AWS cost mechanics and how infrastructure spending changes with scale is beneficial.
- Interest in the creator economy and social data products is a plus.
- Experience should ideally extend beyond analytics, BI, dashboards, or internal reporting into production data systems and customer-facing applications.
Benefits:
- Fully remote position with the flexibility to work from anywhere in Europe.
- Annual salary range of €90,000–€140,000, depending on location, employment type, skills, and experience.
- Stock options in addition to salary, with a significant equity component.
- Unlimited paid vacation.
- Flexible working hours and an async-friendly culture.
- High level of ownership with low bureaucracy and minimal unnecessary meetings.
- Personal development support covering courses, books, conferences, and other learning opportunities.
- Regular team offsites and opportunities to connect with colleagues in person.
- Opportunity to work on large-scale data products with direct customer impact.
- Exposure to modern technologies across AWS, GCP, Spark, Airflow, LLMs, AI agents, lakehouse architectures, and infrastructure as code.
- Opportunity to influence AI-assisted search, recommendations, and intelligent data products from the early stages.
- Collaborative environment with experienced data and backend engineers and strong emphasis on autonomy, feedback, and technical ownership.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
#LI-CL1
