Prinicipal, Data Engineer
Daimler Trucks North America LLC
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Job summary
Mercedes-Benz USA, LLC is seeking a Principal Data Engineer to set architectural direction and build scalable data platforms across analytics, ML, and AI initiatives. You will own complex problem spaces and drive standards, governance, and reusable patterns.
You will lead enterprise data engineering efforts, collaborate with architects, security, and product teams, and mentor engineers to raise data maturity in a fast-paced, multi-cloud environment.
Qualifications
- 8+ years of data engineering, software engineering, or platform engineering experience.
- Experience designing, building, and operating enterprise-scale data platforms or shared engineering capabilities.
- Strong communication, collaboration, and leadership skills.
- Ability to define ambiguous problems and influence through technical trade-offs.
Responsibilities
- Define and evolve enterprise data engineering architecture and standards.
- Design scalable data platforms and data products supporting analytics and ML.
- Build end-to-end data pipelines across lakehouse, warehouse, and data marts.
- mentor engineers and lead cross-functional technical alignment.
Skills
Education
Tools
Job description
Job Description - Prinicipal, Data Engineer (MER00047U4)
Description
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.
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Mercedes-Benz USA is committed to fostering an inclusive environment that appreciates and leverages the diversity of our team. We provide equal employment opportunity (EEO) to all qualified applicants and employees without regard to race, color, ethnicity, gender, age, national origin, religion, marital status, veteran status, physical or other disability, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local law.
Mercedes-Benz USA, LLC
United States of America-Georgia-Atlanta
One Mercedes-Benz Drive One Mercedes-Benz Drive Atlanta 30328
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