Prinicipal, Data Engineer
Summary
The Principal Data Engineer will lead enterprise data architecture and strategy at Mercedes-Benz USA, designing scalable data platforms and pipelines using Azure, Databricks, and distributed processing frameworks. This role involves setting technical standards, mentoring teams, and delivering data products to support analytics, machine learning, and AI initiatives.
About Us
Mercedes-Benz USA is responsible for the marketing, sales, and service of Mercedes-Benz and Maybach products in the United States. In our people, you will find tremendous commitment to our corporate values. Our products and employees reflect this dedication. We are looking for diverse, top-notch individuals to join the Mercedes-Benz team and uphold these hallmarks.
Job Overview
Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Mercedes-Benz USA, you will be part of a group that solves real business and customer problems using data.
We are seeking a Principal Data Engineer to serve as a senior technical leader for enterprise data engineering. This role defines complex problem spaces, sets architectural direction, and delivers scalable, enterprise-grade data platforms and products that enable reporting, analytics, machine learning, AI products, and digital business capabilities. The Principal Data Engineer operates effectively in high-ambiguity environments, owns outcomes and business impact, and establishes standards, frameworks, and reusable engineering patterns adopted across multiple teams and domains.
Responsibilities
Enterprise Data Engineering Architecture & Standards
• Define and evolve enterprise data engineering architecture, design patterns, standards, and best practices across data platforms and products.
• Create reusable engineering frameworks, templates, automation standards, and playbooks that accelerate delivery and improve consistency across teams.
• Influence technology choices for data platforms, cloud-native services, distributed processing, orchestration, CI/CD, monitoring, and reliability engineering.
• Evaluate emerging data engineering and platform technologies that improve scalability, performance, security, cost efficiency, and developer productivity.
Data Platform & Product Delivery
• Design and deliver high-performance, scalable data platforms and data products supporting analytics, reporting, machine learning, AI, and enterprise decision-making use cases.
• Build and modernize end-to-end data pipelines across data lake, warehouse, lakehouse, data mart, and semantic consumption layers.
• Enable data engineers, analysts, data scientists, AI engineers, and business teams through reliable, governed, and reusable data services.
• Support platform capabilities for batch, streaming, event-driven, and API-based data integration patterns.
Operational Excellence, Reliability & Governance
• Identify systemic gaps in data quality, platform reliability, observability, performance, cost, resiliency, and operational readiness, and drive solutions end-to-end.
• Establish best practices for production operations, monitoring, logging, incident response, runbooks, platform support, and continuous improvement.
• Ensure platforms and data products comply with enterprise standards for security, governance, data quality, privacy, and responsible data use.
• Drive automation through metadata management, reusable components, and repeatable engineering practices to reduce manual effort and operational risk.
Collaboration, Influence & Technical Leadership
• Partner with architects, infrastructure, security, analytics, AI/ML, product, and business stakeholders to translate complex business needs into scalable technical solutions.
• Operate in high ambiguity by defining problem statements, success metrics, technical options, trade-offs, and implementation approaches.
• Provide technical mentorship and guidance to engineers, raising data engineering maturity and strengthening engineering excellence across the organization.
• Lead cross-functional technical alignment and influence decisions without relying on formal reporting authority.
Technical Skills & Tools
Required
• Deep expertise in Python, SQL, PySpark and/or Scala, and distributed data processing frameworks.
• Strong experience with Azure cloud platforms and Azure Databricks, including Delta Lake and platform-scale data processing patterns.
• Experience designing and operating data lakehouse, warehouse, data mart, semantic layer, and enterprise analytical data products.
• Experience with CI/CD, workflow orchestration, Git-based development, automated testing, and production release practices.
• Experience with Docker, Kubernetes, Infrastructure as Code, cloud-native deployment patterns, and modern DevOps/DataOps practices.
• Strong understanding of observability, monitoring, logging, performance optimization, reliability engineering, and cost management.
• Knowledge of data governance, data quality, data security, access controls, metadata management, and compliance-sensitive environments.
Preferred
• Experience with streaming technologies, event-driven architectures, message queues, and real-time data integration patterns.
• Familiarity with BI and analytics tools such as Power BI, Tableau, Qlik, or comparable semantic-layer-based data discovery platforms.
• Experience with generative AI, agent-based solutions, vector databases, retrieval technologies, or enterprise AI platforms.
• Experience operating in large-scale enterprise environments with multiple business domains and partner teams.