首席数据工程师 (RWE)
Principal Data Engineer (RWE)
1. 开发数据流程，用于自动化生成患者级数据（“数据产品”），供各类业务利益相关者用于多种目的（例如仪表板、报告、研究）。
2. 在数据从数据供应商/合作伙伴处接入后，对数据集进行下游处理，将“原始”格式的数据转换为可使用的数据结构，这些结构将用于开展真实世界证据（RWE）研究、仪表板及其他数据输出。
3. 将异构的原始医疗数据集转换为可重复使用的数据模型，以支持观察性研究和流行病学研究。
4. 偶尔将定制化或一次性数据集（例如生物标志物、突变）转换为OMOP格式（包括理解哪些内容可以转换为OMOP格式，哪些不能，例如允许对无法转换为OMOP格式的剩余数据进行分析）。
5. 构建符合FAIR原则（可查找、可访问、可互操作、可重用）的数据管道和语义数据工程框架，以提升医疗数据资产的可发现性和互操作性。
6. 创建适用于生成式AI用例的AI就绪数据集。
沟通
7. 与RWE编程团队以外的关键利益相关者（例如流行病学家、统计学家、市场准入/卫生经济学家）进行技术交流，确保全面且详细的了解最终用户需求，并进行详细记录。这包括范围讨论、业务分析，以及将用户口头表达的需求转化为可执行的数据结构。
8. 与RWE编程团队内部的同事进行深入的技术交流，构建生成RWE研究输出和数据产品的所需数据结构；同时在数据工程师技能可以增加附加价值的地方，支持这些团队成员创建研究输出。
9. 与IT部门建立联络并保持持续互动，确保从数据合作伙伴处传入的原始数据符合既定用途（如第1点所述）。
10. 在需要时与分析软件供应商（如Databricks等）的技术人员进行联络。
文档
11. 维护清晰的数据流、模式、管道和流程文档，以方便数据接入、故障排查和审计。
质量、验证与支持
12. 设计并执行详细测试（数据验证和监控）方法，针对由自己或其他团队成员构建的数据结构，以确保数据的准确性和可靠性。
查看英文原文
1. Development of data processes for the automated ongoing generation of patient level data (the “data product”) to be used by various business stakeholders for a variety of purposes (e.g. dashboards, reports, studies).
2. Downstream manipulation of datasets after their onboarding from data vendors/partners from “raw” format as provided into useable data structures that will be used to carry out RWE studies, dashboards & other data outputs
3. Transform heterogeneous raw healthcare datasets into reusable data models supporting observational research and epidemiology studies.
4. Occasional conversion of bespoke / one-off datasets (e.g. biomarkers, mutations) to OMOP format (including an understanding of what can and cannot be converted to OMOP format, e.g. to allow analysis to be carried out on residual data that cannot be converted to OMOP).
5. Build FAIR (Findable, Accessible, Interoperable, Reusable) data pipelines and semantic data engineering frameworks to improve discoverability and interoperability of healthcare data assets.
6. Create AI-ready datasets that can support generative AI use cases.
Communication
7. Technical engagement with key stakeholders (e.g. epidemiologist, statisticians, market access/health economists) from outside the RWE programming team to ensure a full and detailed understanding of end-user requirement is created and carefully documented. This includes scoping discussions, business analysis and translation of verbalised end-user needs into actionable data structures
8. Detailed technical engagement with colleagues from within the RWE programming team to build data structures required for the generation of RWE study outputs and data products; also support those team members in creating the study outputs where the data engineer’s skillset can add incremental value
9. Liaison & ongoing interaction with IT department to ensure that raw datasets inbound from data partners are fit for the agreed purposes (as per bullet point 1 above)
10. Liaise, where required, with technical staff employed by analysis software vendors (databricks etc)
Documentation
11. Maintain clear documentation of data flows, schemas, pipelines, and processes to facilitate onboarding, troubleshooting and auditing.
Quality, Validation & Support
12. Design and carry out detailed testing (data validation and monitoring) approaches for data structures built by self or other members of team to ensure the accuracy and reliability of the data within the data product
13. Troubleshoot any issues encountered with data loading, extraction and transformation (ETL)
10. Work in collaboration with three other members of the Data Engineering team, taking on workload from others as and when required
Required Skills & Qualifications
Domain Expertise
- Strong understanding of Real World Data (RWD) and Real World Evidence (RWE) concepts.
- Ability to assess business requirements and recommend appropriate real-world healthcare datasets for analytical use cases.
- Deep understanding of healthcare data models and healthcare data ecosystems.
- Strong expertise in OMOP CDM v5.4 , v6, including extensions.
- Knowledge of healthcare terminologies and standards such as:
o SNOMED CT
o RxNorm
o ICD-10
o LOINC
o HCPCS/CPT
Data Engineering
- Strong experience in building scalable ETL/ELT pipelines.
- Expertise in:
o Databricks
o PySpark
o Spark SQL
o SQL
o Delta Lake
- Experience working with large-scale healthcare and patient-level datasets.
- Strong understanding of Semantic Data Engineering principles.
- Experience building FAIR-compliant data pipelines.
- Experience with cloud-based data platforms and distributed processing frameworks.
Analytics & Visualization
- Strong Power BI development and data modelling skills.
- Ability to create reusable analytical datasets for dashboards and studies.
- Experience designing AI-ready datasets and analytics data products.
Validation & Quality
- Experience implementing automated data quality frameworks.
- Strong data profiling, validation, and monitoring skills.
- Understanding of healthcare data quality assessment methodologies.
Collaboration & Communication
- Excellent stakeholder management and communication skills.
- Ability to translate complex business requirements into technical solutions.
- Experience working with cross-functional global teams.
Disease Area Knowledge
Exposure to one or more of the following therapeutic areas:
- Oncology
- Respiratory
- Immunology & Inflamation
- Infectious Diseases
Nice-to-Have Skills
- Working knowledge of R programming.
- Experience with sparklyR.
- Experience developing analytical applications using R Shiny.
- Knowledge of common observational research methodologies.
- Familiarity with OHDSI tools.
- Exposure to Azure Data Platform services.
**_Veramed is a B Corp_** _accredited company which means that we use the power of business to build a more inclusive and sustainable economy meeting the highest verified standards of social and environmental performance, transparency, and accountability._
_As an organisation that has people at the heart of it, Veramed is committed to creating a diverse environment and is proud to be an equal opportunities employer. We foster a working culture where employees have integrity, honesty and respect for one another without regard to race, national origin, religion, gender identity or expression, sexual orientation or disability. All qualified applicants will receive equal consideration for employment._