Principal Data Engineer

Responsibilities

• Lead the end-to-end data integration strategy for the Butterfly program, ensuring seamless data movement across CRM, ERP, MDM, CDP, analytics, and downstream platforms.
• Own integration architecture decisions, standards, and patterns across batch, real-time, API-based, event-driven, and file-based integrations.
• Partner with business, product, and application teams to define data exchange requirements and align with enterprise data standards.
• Drive the design, development, testing, and deployment of scalable integration solutions supporting global releases and country rollouts.
• Establish and govern integration design reviews, technical specifications, mapping documents, and interface contracts.
• Coordinate cross-functional teams to manage integration dependencies, risks, and release readiness.
• Serve as the primary technical lead for application onboarding, source-to-target mapping, integration assessments, and data flow design.
• Ensure integration solutions meet performance, reliability, scalability, security, and compliance requirements.

Technical Scope & Expectations (Data Engineering Focus)

• Design and implement enterprise integration solutions using APIs, ETL/ELT pipelines, messaging frameworks, event-driven architectures, and cloud-native integration patterns.
• Lead source system onboarding and integration of commercial, customer, product, consent, and transactional data into Butterfly data products.
• Establish reusable integration frameworks, canonical data models, and standardized mapping approaches to accelerate delivery and reduce complexity.
• Embed data quality controls, reconciliation processes, exception handling, and monitoring capabilities into all integration solutions.
• Collaborate with MDM, Data Product, Analytics, Experience, and Application teams to support a unified enterprise data ecosystem.
• Define integration observability standards, including logging, alerting, monitoring, SLA management, and operational support processes.
• Drive API-first integration strategies and support the governance and lifecycle management of enterprise APIs and data services.
• Lead migration and modernization efforts from legacy integrations to cloud-native architectures leveraging Azure, Databricks, and Microsoft Fabric.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field; Master’s degree preferred.
• 15+ years of experience in enterprise data engineering, technical architecture, analytics, AI platforms, and cloud engineering.
• Proven experience leading architecture decisions, technical strategy, and engineering standards across multiple teams and products.
• Hands-on expertise with Azure, Microsoft Fabric, Databricks, Power BI, and modern cloud-native technologies.
• Strong experience with Data Mesh, Data Federation, Data Products, Data Modeling, Data Governance, and Enterprise Data Management disciplines.
• Demonstrated expertise with Generative AI technologies including LLMs, RAG, vector databases, AI agents, prompt engineering, and enterprise AI architectures.
• Experience implementing MLOps, LLMOps, Responsible AI, model governance, and AI operationalization frameworks.
• Strong understanding of structured and unstructured data architectures supporting enterprise AI and intelligent automation.
• Exceptional communication, collaboration, and stakeholder management skills with the ability to influence both technical and non-technical audiences.
• Proven ability to lead through ambiguity and drive alignment across business and technology organizations.

Preferred Qualifications

• Experience operating in highly regulated industries with stringent security, privacy, compliance, and quality requirements.
• Experience partnering with ISRM, Quality & Compliance organizations, and audit functions to ensure operational readiness and control compliance.
• Experience with enterprise knowledge management platforms, document intelligence solutions, and semantic technologies.
• Familiarity with knowledge graphs, graph databases, vector platforms, enterprise search, and advanced AI retrieval strategies.
• Demonstrated experience establishing engineering observability, reliability engineering practices, Site Reliability Engineering (SRE), and operational excellence frameworks.
• Experience driving measurable business outcomes and value realization through enterprise data and AI initiatives.
• Experience leading enterprise AI transformation programs, AI platform strategy, and adoption of emerging AI technologies at scale.