Define engineering standards and patterns across architecture, code quality, performance, scalability, observability, and governance — and drive their adoption through example and mentorship.
Lead the design and implementation of robust pipelines, semantic models, and data products that power dashboards, self-service analytics, forecasting, and downstream machine learning systems.
Build the production-grade data pipelines and feature/data infrastructure that data scientists rely on to train, serve, and operationalize models.
Diagnose and resolve the toughest performance, reliability, and scalability challenges across BI and data workloads (e.g., Power BI, Synapse, Fabric).
Drive technical strategy and execution across Microsoft Fabric, Synapse, Power BI, and broader Azure BI technologies, evaluating new tools and making build/buy recommendations.
Mentor and grow data engineers and analytics engineers, conducting design reviews and elevating the technical capability of the team.
Act as a senior technical thought partner to engineering and business leadership on data architecture, technical tradeoffs, and platform investment priorities.
Translate complex business problems into practical, scalable, and well-architected data solutions.
What We’re Looking For
Extensive hands-on experience as a senior or principal-level data engineer, with a track record of designing and building production data platforms at scale.
Deep expertise across the Azure BI / data technology stack, including
Microsoft Fabric
Azure Synapse Analytics
Power BI and semantic modeling
Broader Azure data and analytics services
Strong command of data engineering architecture, modern analytics platforms, dimensional and semantic modeling, and scalable, fault-tolerant pipelines.
Expert-level proficiency in Python and SQL, with strong software engineering fundamentals (testing, version control, CI/CD, modular design).
Working understanding of how data scientists consume data, enough to build the pipelines and feature/data infrastructure that support model training and serving.
Demonstrated ability to solve ambiguous, complex business problems through robust technical design and pragmatic execution.
Strong mentoring and influence skills — able to elevate engineering practices and lead technically without formal authority.
Excellent communication skills; able to explain complex technical concepts and tradeoffs to both technical and non-technical partners across Ops, Business, Sales, Marketing, Product, and Engineering.
Ability to thrive in a fast-paced, high-visibility environment with multiple priorities and stakeholders.
Preferred Experience
12+ years experience building data platforms supporting enterprise use cases across operations, commercial functions, and product-driven organizations.
Experience spanning both BI modernization and data platform enablement for analytics and ML within the same platform.
Familiarity with cloud-native engineering practices, infrastructure-as-code, and secure, scalable data environments.
Experience defining platform-wide standards and reference architectures adopted across multiple teams.
Experience building data pipelines that support RAG, LLM, or modern AI workloads on enterprise data.
Why This Role Matters
The Principal Data Engineer is the technical anchor of IAA’s data and AI capability. This person shapes how the company builds and scales its data platform — enabling better decisions, improved business performance, operational efficiencies, and a durable competitive advantage. Through architecture, hands-on engineering, and influence, this leader directly impacts both technical direction and business outcomes across the organization.
