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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