Senior Product Data Engineer

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 Netherlands.

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.