Senior Product Data Engineer

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.

• 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.

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