IQVIA
IQVIA
IQVIA apvieno veselības aprūpes datu analītiku, progresīvas tehnoloģijas un konsultācijas, lai palīdzētu organizācijām risināt sarežģītus izaicinājumus dzīvības zinātņu un veselības aprūpes nozarēs. Tās Connected Intelligence platforma apvieno datus un mākslīgo intelektu, lai atbalstītu klīniskos pētījumus, tehnoloģiju izstrādi un jaunu terapiju attīstību. Uzņēmuma komandas strādā analītikas, konsultāciju un veselības aprūpes tehnoloģiju jomā, piedāvājot iespējas, kas saistītas ar pētniecību, digitālo inovāciju un pacientu ārstēšanas rezultātu uzlabošanu.

Lead Data Engineer at IQVIA – Amsterdam Hybrid

Lead the architecture and engineering of IQVIA’s healthcare data platform in a hybrid Amsterdam role. Build dependable pipelines and warehouses while guiding the team and supporting clinical-data innovation.

Apraksts

  • Own the end-to-end design and delivery of pipeline and warehouse layers across the data platform, using Prefect, Python, and SQL.
  • Advance the platform by standardizing engineering patterns, lowering operational effort, and strengthening maintainability and reliability.
  • Protect continuity and quality during change through release planning, validation approaches, risk controls, and clear communication with technical stakeholders.
  • Work with Product and Engineering to clarify requirements, plan delivery, and prioritize backlog items and incoming work.
  • Strengthen engineering quality through code reviews, disciplined practices, and responsible use of AI-assisted development.
  • Partner with QA to increase test maturity, expand coverage, and automate pipeline and data-transformation testing.
  • Coordinate with Application and AI/ML leads to connect data capabilities across the broader product ecosystem.
  • Lead the data engineering team, including line management when applicable, while coaching engineers and managing work intake and priorities.
  • Support products for healthcare data search, clinical and medical research, and care quality assessment.
  • Collaborate cross-functionally with developers, designers, medical consultants, and machine learning engineers.

Prasības

  • Extensive hands-on Python development experience, with a focus on scalable, maintainable, testable, production-ready software.
  • Experience designing, building, and maintaining ETL/ELT pipelines with Airflow, Prefect, Luigi, or Dagster.
  • Practical dbt experience spanning data transformation, modelling, testing, documentation, and deployment.
  • Experience using Databricks for data engineering workflows and large-scale pipeline optimization.
  • Advanced SQL expertise and hands-on experience with Microsoft SQL Server, PostgreSQL, or comparable enterprise relational databases, including DDL and DML.
  • Experience building and operating cloud data platforms, warehouses, and Lakehouse architectures.
  • Background leading development teams or acting as a senior technical contributor who provides architectural direction and mentorship.
  • Ability to make independent architecture and design choices while balancing scalability, maintainability, performance, and business needs.
  • Strong command of data engineering practices covering quality, lineage, observability, governance, and performance optimization.
  • Experience using automation and AI-assisted development tools securely and under appropriate controls.
  • Experience with DevOps methods, including version control, automated testing, CI/CD, and infrastructure as code.
  • Ability to explain complex technical ideas and business value to both technical and non-technical stakeholders.
  • Experience working in agile teams with four or more developers.
  • Excellent interpersonal, presentation, stakeholder-management, and problem-solving abilities.
  • Strong attention to detail and a consistent focus on high-quality delivery.
  • Exceptional written and spoken English communication skills.
  • Based in Europe, preferably within commuting distance of Amsterdam, Netherlands.

Priekšrocības

  • Flexibility to work from the office, home, or a combination of both.
  • The opportunity to contribute to medical innovation that improves outcomes for patients and healthcare providers.
  • Collaboration with machine learning specialists, including exposure to machine learning and natural language processing.
  • A dynamic, cross-functional team setting.
  • Online collaboration with colleagues across international locations.
  • Professional growth supported by coaching, mentoring, and career development opportunities.

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