Data Engineer

Role Overview

The Data Engineer is an individual-contributor role responsible for building, maintaining and improving reliable data engineering solutions that enable trusted data, analytics and AI across Pharming. The role translates defined business and data requirements and architectural direction into practical data pipelines, integrations, data models and reusable components that support data-driven decision-making and scalable delivery across global functions.

What You Will Do

Build, maintain and enhance scalable batch and real-time data pipelines, ingestion workflows and transformation processes that integrate data from enterprise, scientific, operational and external sources. Develop and maintain data models, curated datasets and reusable data components that provide trusted, well-structured data for analytics, reporting, AI and machine-learning use cases.

Why It Might Be a Fit

The ideal candidate will have at least 4 years of progressive experience in data engineering, data platforms, software engineering or related disciplines, with hands-on experience delivering production data solutions. Experience designing, building and operating scalable data pipelines and data-platform solutions in complex environments is also required.

Requirements

• Relevant bachelor’s or master’s degree in Computer Science, Data Science, Engineering, Information Management or a related field, or equivalent demonstrable professional experience
• At least 4 years of progressive experience in data engineering, data platforms, software engineering or related disciplines, with hands-on experience delivering production data solutions
• Experience designing, building and operating scalable data pipelines and data-platform solutions in complex environments
• Experience in biotech, pharmaceutical or another regulated industry is preferred
• Working experience with modern data architectures and platforms, including data lakes, data warehouses or lakehouse patterns, ETL/ELT, orchestration, APIs, batch and/or streaming integration, and cloud-based data services
• Experience with automated testing, CI/CD, version control and deployment practices for data products and pipelines; infrastructure-as-code exposure is an advantage
• Strong practical skills in SQL and a general-purpose programming language commonly used for data engineering, such as Python, together with sound software-engineering practices
• Good understanding of data modelling, data quality, metadata, lineage, observability, security and access-control principles, with the ability to apply governance requirements in technical implementation
• Demonstrated ability to explain technical decisions to technical and non-technical stakeholders, collaborate across functions and resolve data-engineering problems with appropriate guidance and escalation

Benefits

• Flexible hybrid work schedule to promote a healthy work-life balance
• Competitive compensation package including annual target bonus
• Long-term incentive program
• 401(k) plan with company match
• Paid Time Off (PTO)
• 13 Company Holidays per year
• Excellent benefit plans including medical, dental, and vision
• Flexible spending accounts
• Company-provided life insurance, short-term disability, and long-term disability plans
• Optional accident, hospital indemnity, critical illness, and pet insurance plans
• Tuition reimbursement program
• Health and wellness program
• Choice of company provided mobile phone or cell phone stipend

Tagged as: Data Analysis, Python, SQL