Senior Data Engineer

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Curium is the world’s largest nuclear medicine company with more than a century of industry experience. We develop, manufacture and distribute world‑class radiopharmaceutical products to help patients around the globe. Our proven heritage combined with a pioneering approach are the hallmarks to deliver innovation, excellence and unparalleled service.

With manufacturing facilities across Europe and the United States, Curium delivers SPECT, PET and therapeutic radiopharmaceutical solutions for life‑threatening diseases to over 14 million patients annually. The name ‘Curium’ honors the legacy of pioneering radioactive researchers Marie and Pierre Curie, after whom the radioactive element curium was named and emphasizes our focus on nuclear medicine. The tagline ‘Life Forward’ represents our commitment to securing a brighter future for all those we serve:
An enhanced quality of care for our patients. A trusted partner to our customers. A supportive employer to our valued team.

We are seeking an experienced Senior Data Engineer to design, build, and maintain scalable data pipelines, data platforms, and analytical data products. The Data Engineer will collaborate with data architects, data scientists, analysts, application teams, and business stakeholders to deliver trusted data for analytics, reporting, machine learning, and operational use cases.
The ideal candidate has strong experience with modern cloud data platforms, data integration, data modeling, distributed processing, data quality, and governance. Experience with Databricks, Snowflake, Microsoft Fabric, Azure, or similar cloud platforms is highly desirable.

Work Schedule: M - F 8am - 5pm. This is a hybrid position: 4 days onsite, 1 day remote.

• Design, develop, and maintain scalable batch and real-time data pipelines.
• Build reliable ETL/ELT processes for structured, semi-structured, and unstructured data.
• Integrate data from databases, APIs, SaaS applications, files, and event streams.
• Develop and maintain cloud-based data lake, lakehouse, and data warehouse solutions.
• Work with technologies such as Databricks, Snowflake, Microsoft Fabric, Azure Data Factory, Spark, SQL, and cloud storage.
• Implement reusable data engineering frameworks, patterns, and standards.
• Support data platform modernization and migration initiatives.
• Data Modeling & Data Products:
  • Design logical and physical data models for analytical and operational use cases.
  • Develop dimensional, normalized, and lakehouse data models.
  • Build curated data layers supporting reporting, analytics, and machine learning.
  • Support domain-oriented data products within a data mesh operating model.
• Data Quality, Governance & Security:
  • Implement data validation, quality checks, reconciliation, monitoring, and alerting.
  • Support data cataloging, metadata management, lineage, and data ownership.
  • Implement appropriate security, access controls, privacy, and regulatory requirements.
  • Establish monitoring and operational processes for production data pipelines.
• Performance & Cost Optimization:
  • Optimize data pipelines, queries, storage, and compute workloads.
  • Apply appropriate partitioning, clustering, caching, and workload‑management techniques.
  • Identify opportunities to improve platform performance, scalability, reliability, and total cost of ownership.
• Partner with architects, data scientists, analytics engineers, BI developers, application developers, and business stakeholders.
• Translate business requirements into scalable technical solutions.
• Participate in architecture reviews, technical design sessions, code reviews, and Agile delivery.
• Create technical documentation and operational runbooks.
• Mentor junior engineers and promote…