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

Qualifikationen • Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related technical field, or equivalent practical experience
• 8+ years of progressive experience in data engineering, software engineering, platform engineering, machine learning engineering, or related technical disciplines
• Demonstrated experience designing, building, and operating enterprise-scale data platforms, data products, or shared engineering capabilities
• Proven ability to define ambiguous problems, align stakeholders, make technical trade-offs, and deliver outcomes across teams
• Strong communication, collaboration, stakeholder management, and technical leadership skills
• Self-starter with strong ownership mindset, sound judgment, and the ability to mentor engineers and influence engineering direction

Additional Information
• Must be able to work flexible hours/work schedule
• Travel domestically and internationally as needed
• Work holidays and weekends when required
• Position requires collaboration with business, technology, and external partner teams across multiple time zones
• Enjoys collaborative work and technical mentoring with peers and junior team members