Data Engineer - Data Platform & Ontology (m/f/d)

At

, we’re all about facts and data, for we are the world's leading business data platform. By providing reliable and easy-to-use data as well as various data analytics products and services, we empower people worldwide to make fact-based decisions. Founded in Hamburg in 2007, we have quickly grown into a global company with offices in major cities such as London, New York, Berlin and Tokyo. And we still have a lot of plans. Our constant growth does not only prove our success, but also keeps creating new development and career opportunities for our employees. We value and celebrate our diverse culture. You are welcome here for who you are, no matter where you come from, what you look like, or whether you prefer bar graphs to pie charts. Your story matters – keep writing it as part of our team. Are you ready to join us?

As Data Engineer on our Healthcare Platform, you will own the foundational data ingestion, entity-resolution, and platform infrastructure end-to-end. You will design and operate scalable batch/streaming data pipelines, harden platform orchestration, and ensure system reliability, security, and cost efficiency. A central challenge of this role is building and operationalizing our

—a consistent semantic model covering hospitals, departments, specialties, metrics, and classification standards. Working closely with Analytics Engineers, Data Scientists, and Methodology experts, you will turn heterogeneous, multi-country hospital data into a coherent, highly queryable data asset.

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Build, optimize, and operate reliable ELT pipelines (using Python, SQL, and Prefect/Airflow) to ingest data from heterogeneous international sources, APIs, databases, and lakehouse storage (S3, Apache Iceberg). •

Drive the implementation of entity resolution and master data management (MDM) for international hospital entities, mapping raw source data to canonical structures and maintaining standardized vocabularies (e.g., ICD/OPS, specialty taxonomies). •

Establish strict data contracts (Pydantic, dbt contracts) and schema management, ensuring dataset reproducibility, data lineage tracking, and automated validation across all platform pipelines. •

Optimize data storage, query execution, and compute costs across AWS and Snowflake, keeping data assets performant, secure, and cost-effective. •

Implement automated testing and deployment workflows for data pipelines using GitHub Actions and Infrastructure as Code (Terraform). •

Partner directly with Analytics Engineers, Data Scientists, and domain experts to deliver documented, research-grade, and production-ready datasets.

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Advanced Python and analytical SQL for complex data ingestion across diverse file formats, REST APIs, databases, and cloud lakes (S3/Iceberg). Hands-on experience with modern orchestrators (Prefect, Airflow, or Dagster). •

Practical experience with entity resolution/record linkage frameworks (e.g., Splink, dedupe, recordlinkage) and schema management/data contracts (Pydantic, dbt contracts, or JSON Schema). •

Deep hands-on experience in an AWS production environment (S3, ECS/EC2) combined with cloud data warehouses (Snowflake). •

Proven track record of automating data pipeline deployments, integration tests, and validation workflows via GitHub Actions.

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Exposure to ontology/semantic frameworks (RDF/OWL, SKOS, Neo4j, LinkML) or international medical classifications/vocabularies (SNOMED CT, ICD/OPS, FHIR). •

Experience operating metadata registries and lineage catalogs (e.g., OpenMetadata, DataHub, dbt docs). •

Proficiency in using Terraform to declaratively manage cloud resources and environments.

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Bachelor's or Master's in Computer Science, Data Science, Software Engineering, or a related quantitative field. •

3+ years in data engineering building production pipelines and data platforms; including a sustained period within one organization seeing a core platform or product through build → launch → iteration. •

Healthcare domain experience is a plus (basic understanding of healthcare KPIs, quality metrics, or benchmarking concepts; familiarity with hospital structures and medical classification systems like ICD/OPS is especially valuable). •

Strong analytical and systems mindset, with a proven ability to transform messy, heterogeneous international data into a clean, well-governed, and highly structured data asset. •

Fluent in English, German is a plus. •

Highly structured, curious, detail-oriented, and motivated to collaborate closely with analytics engineers, data scientists, and methodology experts in an international environment.