Senior Data Engineer, Product Analytics

Pagero, part of Thomson Reuters, provides a global e-invoicing and business document network that connects more than 14 million businesses across 75+ countries. Pagero is recognized by IDC MarketScape as a Leader in European compliant e-invoicing. As Pagero becomes more integrated with the ONESOURCE ecosystem, the company is expanding its product analytics and data engineering capabilities.
We are hiring a Senior Data Engineer to build the data foundation Pagero’s product analytics runs on. That foundation grew up alongside a fast-scaling network and now needs to be re-platformed for scale: event-driven collection at the source, pipelines with real observability, and product data that reaches Thomson Reuters’ shared data environment so teams beyond Pagero can use it.

About the Role
As Senior Data Engineer, Product Analytics, you will be responsible for:Build and Operate the Data Pipelines: Design, build, and run the pipelines and data models that turn Pagero’s transaction, network, and customer data into analytics-ready tables the team and its stakeholders can depend on.
Instrument Product Behavior: Work with Pagero engineering teams to implement event-driven data collection across the customer journey: onboarding, transaction success, compliance checkpoints, and trading partner connectivity. Contribute to event definitions and data contracts, treating the teams that own each service as design partners.
Land Pagero Data in the Shared Environment: Build the ingestion path that delivers aggregated Pagero product data into Thomson Reuters’ enterprise data lake, so central data and commercial analytics teams can consume it and end to end customer journey tracking becomes possible.
Own Reliability and Observability: Build the monitoring, alerting, data quality testing, and lineage that make the platform dependable in a high volume regulated transaction environment. When something breaks, the system should say so before a stakeholder does.
Automate What Is Manual: Convert recurring reporting into automated, self-serve data products, including the customer-level reporting that enterprise customers with customized implementations rely on.
Engineer With AI as Standard Practice: Use Claude, GitHub Copilot, Cursor, and MCP-based workflows in day-to-day pipeline and analysis work, with human review and QA to hold quality. Model and expose Pagero data so agents and self-serve tools can reason over it reliably.
Work Across Boundaries: Partner with Pagero’s analysts on what the data needs to answer, and with Pagero engineering, TR data platform, and security stakeholders on how it gets delivered. Explain trade-offs clearly to people who do not work in data.
Document and De-risk: Leave behind documented pipelines, models, and runbooks. Institutional knowledge should live in the repository, not in one person’s head.
About YouRequired Experience and

Skills:
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field.5+ years in data engineering or analytics engineering, including ownership of production pipelines other people depend on.
Strong SQL and Python, with a solid grounding in data modeling, software engineering practice, version control, testing, and code review.
Hands-on experience building and orchestrating batch and event-driven pipelines (dbt, Airflow, Kafka, or equivalent) against high volume transactional data.
Experience with cloud data platforms (Snowflake, GCP, AWS, Azure, or equivalent) and the practical trade-offs between them.
Experience re-platforming data from on-premise or legacy environments into a cloud or enterprise lake environment.
Demonstrated ownership of data reliability: monitoring, alerting, data quality testing, and lineage, in an environment where a silent failure has real consequences.
Hands-on experience with AI tooling: LLMs, AI coding assistants (Claude, GitHub Copilot, Cursor, or equivalent), or MCP-based workflows used to accelerate engineering and analysis work.
Experience partnering with product engineering teams on instrumentation or event design, including negotiating what gets emitted rather than only consuming what already exists.
Clear written and verbal communication, and the judgment to ask clarifying questions early.
Based in Gothenburg, Sweden; with the right to work there.
Preferred Skills and

Experience:
Product sense: an instinct for which metrics change a product decision and which only fill a dashboard. Experience in or alongside product teams, and SaaS experience, are assets.
Experience working through data residency, sovereignty, or privacy requirements (GDPR or equivalent) in a practical architecture context.
Experience with e-invoicing, VAT compliance, or B2B transaction platforms.
Experience in a post-acquisition or integration environment.#LI-LT1What’s in it For You?Hybrid Work Model: We’ve adopted a flexible hybrid working environment (2-3 days a week in the office depending on the role) for our office-based roles while delivering a seamless experience that is d