Senior Principal Data Engineer

Accountabilities

• • Design and evolve enterprise-scale data platform architectures that support analytical, operational, and AI workloads.

  • Define reusable engineering frameworks, reference architectures, design patterns, and technical standards focused on scalability, security, performance, reliability, and maintainability.

  • Establish long-term platform strategies and influence technical direction across multiple business domains and engineering teams.

  • Lead architecture reviews, technical design sessions, and engineering governance for strategic and highly complex initiatives.

  • Partner with engineering leadership to define technology roadmaps and promote consistent engineering practices.

  • Design scalable ETL/ELT frameworks, enterprise data pipelines, and conceptual, logical, and physical data models.

  • Develop reusable, metadata-driven data integration frameworks and establish standards for data modeling, metadata management, lineage, and performance optimization.

  • Champion AI-assisted software development and evaluate emerging AI coding assistants and engineering productivity tools.

  • Develop and promote AI-powered engineering accelerators, including code generation, automated documentation, code-review assistance, test generation, data-quality validation, and engineering copilots.

  • Provide technical leadership across cloud and platform technologies including Databricks, Azure, AWS, Docker, Kubernetes, Python, SQL, Git, REST APIs, and CI/CD.

  • Design secure, governed data platforms incorporating metadata, lineage, role- and attribute-based access controls, observability, and other enterprise controls.

  • Establish engineering standards covering code quality, testing, automation, observability, reusable components, and operational excellence.

  • Mentor senior and principal engineers and promote a culture of technical excellence, continuous learning, collaboration, and innovation.

  • Collaborate with Product Management, Data Science, Analytics, Data Governance, Enterprise Architecture, and Infrastructure teams to deliver strategic data capabilities.

  • Serve as a trusted technical advisor to engineering and business leadership, influencing complex decisions through expertise and collaboration rather than direct authority.

  • Drive improvements in platform scalability, reliability, security, engineering productivity, reusable architecture, and adoption of AI-enabled development practices.

  Requirements

  • Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical discipline; a Master’s degree is preferred.

  • 7+ years of experience in Data Engineering, Software Engineering, Data Platform Engineering, or a closely related field.

  • Extensive experience designing and implementing enterprise-scale cloud data platforms and data architectures.

  • Expert-level programming skills in Python and SQL.

  • Strong expertise in conceptual, logical, and physical data modeling.

  • Hands-on experience with Databricks, Azure, AWS, Docker, Kubernetes, Git, REST APIs, and CI/CD environments.

  • Strong understanding of software architecture, design patterns, distributed systems, and secure data platform design.

  • Experience establishing engineering standards, governance practices, reusable frameworks, and technical roadmaps.

  • Demonstrated ability to lead highly complex technical initiatives through influence, collaboration, and technical credibility rather than direct people management.

  • Excellent communication, stakeholder-management, presentation, and technical leadership skills.

  • Strong analytical thinking, problem-solving ability, initiative, and results orientation.

  • Demonstrated ability to mentor experienced engineers and foster knowledge sharing and engineering excellence.

  • Experience working collaboratively across Product, Data Science, Analytics, Governance, Architecture, and Infrastructure functions.

  • Preferred experience in pharmaceutical, biotechnology, healthcare, or another regulated industry.

  • Preferred experience with Databricks Unity Catalog, enterprise data governance, metadata management, data lineage, and observability.

  • Experience with Infrastructure as Code tools such as Terraform, Bicep, or comparable technologies is preferred.

  • Experience applying Generative AI to software engineering or data engineering is preferred.

  • Commitment to continuous learning, adaptability, inclusive collaboration, clear communication, and innovation.

  Benefits

  • Annual base salary range of $150,900–$195,900, with actual compensation determined by factors including skills, experience, education, certifications, training, and work location.

  • Eligibility for an annual bonus plan for applicable non-commercial roles.

  • Eligibility for incentive compensation for applicable commercial roles.

  • Discretionary equity award opportunities.

  • Employee Stock Purchase Plan.

  • Medical, dental, and vision insurance.

  • 401(k) retirement plan.

  • Flexible Spending Account (FSA) and Health Savings Account (HSA) options.

  • Life insurance.

  • Paid time off.

  • Wellness benefits and programs.

  • Fully remote U.S. work arrangement.

  • Opportunities to influence enterprise technology strategy and work with modern cloud, data, and AI technologies.

  • Opportunities for technical leadership, mentoring, continuous learning, and professional development.