About the Job
As a Data Engineer Manager in the Healthcare and Life Sciences (HCLS) domain, you will play a pivotal role in data integration, transformation, and mapping success patterns. Collaborating with Health and Wellbeing, Tech/Digital, and other NEOM sectors, your goal is to ensure the right data reaches the right people at the right time. You will also be responsible for developing robust, efficient, and reliable data pipelines.
Key Accountabilities & Activities
- Build and manage reusable, standardized data pipelines and production-grade datasets.
- Standardize physical storage and search (indexing system).
- Oversee schema management, including data, metadata, versioning, provenance, and governance.
- Implement end-to-end solutions using programming languages (e.g., Python, Scala, or Go) and open-source tools (e.g., Spark, Elasticsearch) within an on-premise environment.
- Create analytics tools leveraging the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
- Develop infrastructure for optimal data extraction, transformation, and loading from various sources using ETL/ELT tools.
- Document processes and maintain information in wikis or other documentation systems.
- Collaborate with stakeholders, including the Executive, Product, Data, and Design teams, to address data-related technical issues and support their infrastructure needs.
- Lead the strategy for data analytics across different healthcare areas, focusing on computational biology and medicine, and influence change through negotiation and collaboration.
- Be hands-on and guide the team in utilizing the correct data analytics technology stack.
- Develop strategic partnerships with advanced healthcare actors.
- Support the Digital Health Director in cultivating relationships with relevant internal and external stakeholders.
- Build and develop a team of data engineers.
- Prepare relevant progress reports and provide regular updates to management regarding project statuses.
- Address challenges and risks proactively, assisting divisions in developing suitable mitigation strategies.
- Identify risks related to various aspects of the sector and collaborate with leadership on management plans.
Background, Skills & Qualifications
- Strong experience in data modeling to design and implement complex, scalable data schemas optimized for performance.
- Familiarity with cloud-based data warehousing platforms, NoSQL and SQL.
- Experience in software engineering and DevOps principles, including knowledge of Git, GitLab, Terraform, and container technologies (e.g., Docker, Kubernetes).
- Proficiency in Python and R, distributed computing technologies (e.g., Spark, PySpark), and Linux Bash scripting.
- Understanding of workflow management technologies and modern data streaming (e.g., Kafka).
- Familiarity with Machine Learning, Data Science, and AI technologies.
- Excellent problem-solving, communication, and presentation skills, with the ability to introduce new technologies to the organization.
- Proficiency in agile methodologies.
- Prior experience in the healthcare industry, particularly in Clinical/EHR environments, is essential.
- Knowledge of clinical research and principles of project management.
- Understanding of Health Data Exchange Standards, such as HL7 and FHIR.
Qualifications
- Master’s degree in Computer Science, Software Engineering, or a related technical/scientific field is preferred; a PhD is an asset.
- 8-10 years of experience in Data Engineering within the Clinical, pharmaceutical, or biotechnology fields.
- Experience in Data Engineering specifically in Healthcare or Life Sciences.
- Cloud experience with platforms like GCP, Azure, or equivalent.
- Project Management Professional (PMP) certification is advantageous.
