Warren Data Engineer NJ

Job description

Summary 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 to ensure data solutions are secure, governed, maintainable and fit for purpose. More complex architectural, cross-domain or high-risk decisions are escalated through the appropriate technical and governance channels. This is a hybrid role based in our Warren, NJ office with an expectation of 2-3 in office days in office each week and the flexibility to adjust schedules depending on business needs. What you'd do Data engineering and platform delivery 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. Translate approved enterprise architecture and data-platform direction into practical engineering implementations using established patterns for storage, processing, orchestration, integration and access. Support reliability, performance and maintainability by implementing automated testing, monitoring, observability, error handling, documentation and operational support practices. Apply modern software-engineering practices to data solutions, including version control, code review, CI/CD and automation; use infrastructure-as-code where appropriate and established by the platform standards. Contribute to continuous improvement of data-engineering methods, reusable components and platform capabilities to reduce duplication and improve delivery efficiency. Data quality, governance and integration Implement data-quality controls, validation rules, lineage and monitoring that improve 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 practical technical controls. Build and maintain secure data integrations across applications, platforms and external data sources using approved APIs, interfaces, event-driven or file-based patterns. Apply privacy, cybersecurity, access-control, retention and compliance requirements in data pipelines and platform solutions in collaboration with the relevant control functions. Support metadata, master/reference data, interoperability and lifecycle-management practices that improve discoverability, consistency and reuse of data across functions. 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. Technical delivery and collaboration 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 be transitioned 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 and provide practical input on technical fit, scalability, risk and lifecycle considerations. Contribute to knowledge sharing, technical documentation, communities of practice and team capability development through active collaboration and peer support. Other duties as assigned What you'd need 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. Fluent English required; ability to collaborate effectively across global teams and time zones. Commitment and alignment to Pharming's mission, core values and behaviors. Who we are We’re built on a simple belief: people living with rare diseases deserve more. Pharming is a global biotechnology company taking on some of the toughest rare disease challenges. We develop and commercialize innovative therapies for rare and ultra-rare immunological and genetic diseases with significant unmet need, where our scientific and commercial expertise, long-term commitment, and real partnership can help advance care. We put patients at the heart of our work. Their insights, along with those of caregivers, clinicians, scientists, and parters, shape our strategy, guide our decisions, and help turn science into meaningful progress. We keep it simple, act with urgency, and get it done – expanding access to approved therapies, advancing high-value pipeline programs, and building a leading global rare disease company. Our vision is simple: a world where people living with rare diseases don’t wait for progress or care. Together with the communities who inspire us, we’re opening new possibilities – proving that breakthroughs aren’t just discovered. They’re created together. Ready to make a difference? Join us. What it’s like to work here At Pharming, every role helps move rare disease therapies closer to the people who need them. We make it happen together, across teams, disciplines, and borders, because breakthroughs don’t happen in silos. Rare diseases are complex, and meaningful progress takes people who ask better questions, challenge each other, and turn insight into action. Here, you won’t work on the sidelines. Pharming is a place for builders, problem-solvers, and bold thinkers who move with urgency,