Principal, Data Engineering & Architecture

Job Description

The Principal, Data Engineering and Architecture is a senior individual contributor who leads the end-to-end engineering of enterprise-grade data applications, combining hands‑on development with strategic architecture. Working at the intersection of Data architecture, engineering, and modern software delivery, this role is directly accountable for engineering robust, scalable solutions, operationalizing Architecture as Code, and embedding Agentic AI capabilities into the enterprise data ecosystem, with robust CI/CD integration.

The incumbent will collaborate with cross-functional teams—data engineers, product managers, ML engineers, and business stakeholders to accelerate innovation through hands‑on engineering, infrastructure-as-code, and production‑ready deployments. This role sets technical direction across multiple teams, establishes engineering standards (opinionated frameworks, patterns, and reusable templates), and ensures every data solution is built with security, observability, and operational resilience by design. The principal will conceive and portray the big picture for enterprise data, analyze current state, conceptualize desired future state with the Enterprise Platforms, and define the architecture governance and solution roadmap to close the gap.

As a practicing data architect, the incumbent will design data product reference architectures, formalize Data Contracts as governed architecture artifacts, and position the Semantic Layer as a first‑class tier in the enterprise stack, codifying these patterns into the TRM and Reference Architectures, while conducting architecture reviews to ensure domain teams build in conformance.

Hands‑on engineering of high‑quality data and AI applications end‑to‑end, from conceptual design through build, deployment, and production hardening.

• Build the enterprise data management framework that defines how data is stored, consumed, integrated, and governed by different data entities and applications—ensuring alignment with the Enterprise Data Reference Architecture (ERDA).
• Drive the Data Architecture Strategy for domain‑oriented Data Hubs and Data Products, self‑service data analytics, with established best practices and capabilities for operationalizing data products and services.
• Design data product reference architectures—define the canonical patterns, blueprints, and boundary definitions that data product teams follow when building, publishing, and consuming data products across domains.
• Model and formalize Data Contracts as architecture artifacts—author contract specifications (schema, SLA, semantic) as versioned, machine‑readable architecture documents that sit alongside Architecture Decision Records and are governed through the same review process.
• Architect the Semantic Layer within the enterprise data architecture, to position semantic layer as an architectural tier in the enterprise stack, defining how metric definitions, business logic, and dimensional models are authored, versioned, and consumed consistently by BI, AI/ML, and application layers.
• Define data product boundary and integration patterns—codify architectural guidance on how data products expose interfaces (APIs, event streams, materialized views, semantic endpoints), how contracts govern those interfaces, and how consumers discover and bind to them.
• Create data product and contract standards—codify approved technologies, frameworks, and tooling for data product construction, contract definition, semantic layer implementation, and contract validation between producers and consumers—schema contracts (structure, types, constraints), SLA contracts (freshness, availability, latency), and semantic contracts (business definitions, lineage, classification) — validated automatically within CI/CD pipelines through contract testing.
• Architect and implement a unified Semantic Layer—a centralized business‑logic tier that provides consistent metric definitions, dimensions, and governed data access across BI tools, AI/ML models, and application APIs, eliminating metric drift and conflicting business definitions.
• Integrate contract testing and semantic validation…