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.

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