Principle Data & IT Platform Engineer

Responsibilities

• Define the technology vision, roadmap, and architectural standards for the enterprise data and analytics platform, ensuring tight alignment with business goals and establishing the patterns other engineers build upon.
• Design and lead the build of a modern, Azure/SAP-based data platform leveraging Microsoft Fabric (OneLake, Data Factory, Warehouse) and SAP BDC, integration patterns, setting the foundation for enterprise-wide analytics and reporting at scale.
• Own the design and deployment of dimensional and semantic data models and curated datasets that deliver performant, governed, self-service reporting—establishing modeling standards adopted across the organization.
• Establish enterprise data governance - data quality, security, lineage, and compliance controls across the platform - to guarantee trusted, audit-ready analytics.
• Determine Azure and SAP cohesion/coexistence to determine right fit per use case.
• Design and lead the build of Fabric data pipelines for batch, CDC, and incremental ingestion, architecting reusable patterns for transforming on-premises relational data into the cloud platform.
• Architect integration frameworks and ingestion patterns for time-series, PI historian, SAP Business Data Cloud (BDC)/Datasphere/CPI, Globalscape MFT, Microsoft BizTalk, and API/streaming sources, ensuring reliable handling of high-volume and real-time data as a reusable capability.
• Establish a decision-making framework for SAP Business Data Cloud and the Microsoft Azure stack, defining clear criteria for when to leverage SAP-native data capabilities versus Azure-based data platform services based on data ownership, integration complexity, governance requirements, scalability, cost.
• Define and own the CI/CD strategy, building and governing deployment pipelines that automate releases of infrastructure, data pipelines, and warehouse artifacts—with automated testing, environment promotion, and rollback strategies for safe, reliable delivery.
• Partner with and provide technical guidance to analysts and report developers, translating reporting requirements into scalable warehouse structures and optimized query performance.
• Provide estimation and planning—timelines, resources, and budgets—for architecture initiatives, providing actionable guidance to leadership and stakeholders.

Qualifications

• Bachelor’s degree (Master’s preferred) in Computer Science, Information Systems, or a related field (or equivalent experience).
• 15+ years architecting and hands-on building scalable, secure, and cost-effective cloud-based data and AI platforms, with a track record of delivering centralized, enterprise-wide capabilities.
• 5+ years deep, hands-on expertise with modern cloud data platforms (Azure strongly preferred), including Microsoft Fabric OneLake, data warehousing, and AI/ML architecture.
• Mastery of diverse data stores—relational, NoSQL, and graph databases—with the judgment to select the right technology for each workload.
• 10+ years of designing and operating enterprise integrations across relational databases, SAP BDC/Datasphere/HANA/CPI, Globalscape MFT, Microsoft BizTalk, message buses, and OData/REST APIs.
• 5+ years experience with the Azure service ecosystem, including Azure Functions, Service Bus, Event Grid, API Management, and Container Instances.
• 15+ years demonstrating ability to define technology roadmaps, align architecture to business objectives, and deliver actionable guidance to both technical teams and executive stakeholders.
• 5+ years of enterprise BI, particularly Power BI, including semantic modeling and governed self-service reporting.
• 5+ years integrating and working with time-series historians, including PI historian and related operational technology data platforms.
• Working knowledge of Generative and Agentic AI capabilities, architectural patterns, and supporting technology stacks.
• Disciplined software-engineering practices, including Git-based version control and automated deployment pipelines (CI/CD).
• Proficiency writing clean, reusable, performant code in SQL, Python, and PySpark for data engineering, transformation, and integration tooling.