The Principal Data Platform Engineer leads the architecture and development of the enterprise data platform, focusing on analytics, data science, and AI/ML capabilities. They establish standards for data quality and observability, design data ingestion and processing patterns, and mentor team members to enhance technical capabilities.
Position Summary
The Principal Data Platform Engineer is a senior individual contributor who defines and owns the technical vision, architecture, and evolution of the enterprise data platform. This role is responsible for platform-wide design decisions that enable trusted analytics, business intelligence, and AI/ML use cases at scale.
Serving as the technical leader for data platform and data engineering capabilities, this role designs and governs scalable, reliable, and well-modeled data assets that support analytics, data science, and AI workloads. The Principal Data Platform Engineer partners closely with delivery leadership and hands-on practitioners across the Data and AI organization to ensure the platform balances near-term delivery needs with long-term scalability, reliability, and maintainability.
Operating across multiple scrum teams, this role acts as a force multiplier by establishing standards, reusable patterns, and self-service capabilities that improve data quality, accelerate delivery, and increase the overall effectiveness of analytics and AI initiatives.
Primary Duties & Responsibilities
- Own the technical architecture and long-term roadmap of the enterprise data platform supporting both Analytics/BI and AI/ML workloads.
- Design and evolve data ingestion, transformation, and orchestration patterns that support scalable, reliable, and auditable data pipelines.
- Define and enforce standards for data modeling, including curated analytical datasets, semantic models, and ML-ready / feature-ready datasets.
- Lead platform and architectural design reviews across multiple cross-functional scrum teams, influencing solutions without direct authority.
- Establish platform patterns for data quality, observability, lineage, and reliability to ensure trust in downstream analytics and AI systems.
- Partner with AI Engineers and Data Scientists to enable efficient feature engineering, model training, and inference through well-designed data assets.
- Serve as the technical authority for Microsoft Fabric, Power BI, and associated data platform components, ensuring best practices are consistently applied.
- Enable self-service analytics and data science by delivering reusable data products, documentation, and clear consumption contracts.
- Mentor data engineering team members, raising the overall technical maturity of the organization.
- Balance immediate delivery needs with long-term platform scalability, performance, and maintainability considerations.
- Evaluate and recommend new platform capabilities, tools, and architectural approaches aligned with organizational strategy.
Qualifications
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.
- 10+ years of experience designing and building modern data platforms in production environments.
- Deep expertise in data architecture, data modeling, and distributed data processing for analytics and AI/ML use cases.
- Strong experience with modern cloud data platforms, including managing and optimizing compute, storage, networking, security, and cost governance; Microsoft Fabric and Power BI experience is highly valued.
- Proven ability to design platforms that support both BI/analytics workloads and ML/AI pipelines at scale.
- Experience influencing architecture and standards across multiple teams without direct people management responsibility.
- Strong understanding of data quality, observability, governance, and reliability practices in enterprise environments.
- Adept at partnering with CloudOps, Security, IT, AI Engineering, and Data Engineering teams to ensure the cloud platform supports both current and future needs.
- Excellent communication skills with the ability to engage both technical and non-technical stakeholders.
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