Data Engineer

Pharming Group N.V.•Warren Township, NJ

8d•$115,000 - $155,000•Hybrid

About The Position

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. Reporting to the Head of AI, Data & Analytics, the Data Engineer works independently on assigned data-engineering activities of moderate to high complexity within the AI & Transformation domain. The role applies established engineering standards, contributes to technical design decisions, resolves day-to-day engineering issues, and collaborates with data owners, data stewards, architecture, cybersecurity, privacy, quality, compliance, applications, and business stakeholders. Solutions should be secure, governed, maintainable, and fit for purpose. More complex architectural, cross-domain, or high-risk decisions are escalated through appropriate technical and governance channels. This is a hybrid role based in the Warren, New Jersey office, with an expectation of 2–3 in-office days each week and flexibility to adjust schedules depending on business needs. The position supports Pharming’s digital and technology transformation by helping develop scalable, resilient, and future-ready capabilities across the organization.

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, including hands-on delivery of production data solutions.
Experience designing, building, and operating scalable data pipelines and data-platform solutions in complex environments.
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.
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, and 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.
Fluent English, with ability to collaborate effectively across global teams and time zones.
Commitment and alignment to Pharming’s mission, core values, and behaviors.

Nice To Haves

Experience in biotechnology, pharmaceuticals, or another regulated industry is preferred.
Experience with automated testing, CI/CD, version control, and deployment practices for data products and pipelines.
Exposure to infrastructure-as-code.

Responsibilities

Build, maintain, and enhance scalable batch and real-time data pipelines, ingestion workflows, and transformation processes integrating enterprise, scientific, operational, and external data sources.
Develop and maintain data models, curated datasets, and reusable data components for analytics, reporting, AI, and machine-learning use cases.
Translate approved enterprise architecture and data-platform direction into engineering implementations using established patterns for storage, processing, orchestration, integration, and access.
Support reliability, performance, and maintainability through automated testing, monitoring, observability, error handling, documentation, and operational support practices.
Apply software-engineering practices to data solutions, including version control, code review, CI/CD, and automation; use infrastructure-as-code where appropriate and consistent with platform standards.
Improve data-engineering methods, reusable components, and platform capabilities to reduce duplication and improve delivery efficiency.
Implement data-quality controls, validation rules, lineage, and monitoring to increase confidence in data used for business decisions, analytics, and AI.
Work with data owners, data stewards, and business subject-matter experts to translate data definitions, quality expectations, and governance requirements into technical controls.
Build and maintain secure data integrations across applications, platforms, and external data sources using approved APIs, interfaces, event-driven patterns, or file-based patterns.
Apply privacy, cybersecurity, access-control, retention, and compliance requirements to data pipelines and platform solutions in collaboration with relevant control functions.
Support metadata, master/reference data, interoperability, and lifecycle-management practices that improve data discoverability, consistency, and reuse.
Support regulated and GxP-relevant data needs where applicable by ensuring engineering solutions are traceable, controlled, and aligned with Pharming’s quality and validation requirements.
Contribute technical expertise to data initiatives, follow established engineering standards and guardrails, and raise design risks or exceptions for review where appropriate.
Partner with AI, analytics, and business teams to ensure data foundations support analytics, AI, and automation use cases and can move from development into reliable production use.
Collaborate with Enterprise Architecture, Enterprise Applications, and Global IT Operations & Service Delivery to align data solutions with application, integration, cloud, infrastructure, and service-management requirements.
Assess data-engineering technologies, tools, and vendor capabilities, providing practical input on technical fit, scalability, risk, and lifecycle considerations.
Contribute to knowledge sharing, technical documentation, communities of practice, and team capability development through collaboration and peer support.
Perform other duties as assigned.

Benefits

Flexible hybrid work schedule to promote 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.
Medical, dental, and vision benefit plans.
Flexible spending accounts.
Company-provided life insurance and short-term 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.