About Us

Mercedes-Benz USA is responsible for Marketing, Sales and Service of all 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'll be part of a group who love to solve real problems and meet real customer needs.

We are seeking an experienced Senior Data Engineer who will be part of a team building a robust data platform enabling end-to-end capabilities for Reporting, Machine Learning, Generative AI, and Agent-based AI products supporting a variety of users including data engineers, analysts, scientists, AI engineers, and internal/external business partners.

The Principal Data Engineer is a senior technical leader responsible for defining complex problem spaces, setting architectural direction, and delivering scalable, enterprise-grade data platforms and products. This role operates effectively in high-ambiguity environments, owns outcomes and business impact, and establishes standards and frameworks adopted across multiple teams and domains. The Principal DE plays a critical role in bridging traditional data engineering with modern AI/GenAI data infrastructure, ensuring the data platform evolves to serve both analytics and AI workloads at enterprise scale.

 

Responsibilities

Data Platform Architecture & Engineering (40%)

• Define and drive enterprise data engineering architecture, standards, and best practices across the organization.
• Design and deliver scalable, high-performance data platforms supporting analytics, machine learning, and generative AI use cases.
• Architect data lake, warehouse, and lakehouse solutions using Databricks, Delta Lake, and Unity Catalog.
• Design and deliver data platform capabilities that support generative AI and agent-based workloads, including embedding pipelines, vector store integration, and knowledge base management.
• Identify systemic gaps in data quality, reliability, performance, and cost, and drive solutions end-to-end.
• Establish reusable frameworks, patterns, and playbooks for data pipeline development adopted across teams.
• Define data partitioning, optimization, and caching strategies for high-volume and low-latency workloads.
• Lead technical design reviews and architecture decision records (ADRs) for major data platform initiatives.
   

AI/GenAI Data Infrastructure (20%)

• Design and implement data architectures that serve AI/ML model training, RAG pipelines, and agent-based systems.
• Build and govern embedding pipelines, vector database ingestion workflows, and knowledge base refresh processes.
• Define data quality, freshness, and governance standards for AI-consumed datasets.
• Architect unstructured data processing pipelines (document parsing, chunking strategies, metadata enrichment) for GenAI consumption.
• Collaborate with LLMOps and AI Solutions engineers to define data contracts and integration patterns between data platform and AI systems.
• Evaluate and integrate emerging data technologies that support AI workloads (vector databases, graph databases, semantic search).
  
   

Technical Leadership & Strategy (20%)

• Operate in high-ambiguity environments by defining problem statements, success metrics, and technical approach.
• Influence technology decisions and contribute to organizational data and platform strategy.
• Mentor and coach data engineers across the team, establishing technical growth paths and skill development.
• Drive adoption of engineering best practices including code review standards, testing frameworks, and CI/CD maturity.
• Represent data engineering in cross-functional planning with AI engineering, analytics, and business teams.
• Evaluate emerging technologies, frameworks, and engineering approaches for potential adoption.
• Contribute to enterprise-wide data strategy, platform roadmap, and architecture governance discussions.
  
   

Operational Excellence & Governance (10%)

• Define and enforce data governance policies including classification, access control, lineage, and retention.
• Establish monitoring, alerting, and SLA frameworks for critical data pipelines and data products.
• Drive incident response processes, root cause analysis, and preventive measures for data platform issues.
• Implement cost optimization strategies for cloud data infrastructure and compute resources.

• Ensure compliance with regulatory requirements and responsible data practices.

   

   

Collaboration & Stakeholder Engagement (10%)

• Partner with business stakeholders, product owners, and domain experts to translate business needs into data solutions.
• Align with AI engineering teams (LLMOps, AI Solutions) to ensure seamless data-to-AI integration.
• Communicate technical decisions, trade-offs, and roadmap to both technical and non-technical audiences.

• Facilitate knowledge transfer and documentation practices across the data engineering team.

   

   

Day-to-Day Activities

A typical week in this role involves a blend of strategic architecture work, hands-on engineering, mentorship, and cross-functional collaboration:

• Designing data models and pipeline architectures for new business requirements, drawing architecture diagrams and writing technical design documents.
• Writing and reviewing complex data transformation code in Databricks (Python, Spark, SQL) for high-volume and high-complexity use cases.
• Leading technical design sessions with the team to solve ambiguous data challenges and establish patterns for reuse.
• Reviewing pull requests and providing architectural guidance and mentorship to team members.
• Meeting with AI engineering teams (LLMOps, AI Solutions) to align on data contracts, embedding pipeline requirements, and vector store integration.
• Investigating and resolving complex production issues including performance bottlenecks, data quality degradations, and pipeline failures.
• Evaluating new tools and technologies (e.g., vector databases, streaming frameworks, governance tooling) through proof-of-concepts.
• Participating in architecture review boards and contributing to enterprise data strategy discussions.
• Defining and tracking data platform KPIs (pipeline reliability, data freshness, query performance, cost efficiency).
• Conducting 1:1 mentoring sessions with junior and mid-level data engineers on technical growth.
• Collaborating with data governance and security teams on access policies, data classification, and compliance requirements.
• Writing and maintaining engineering playbooks, architectural decision records, and onboarding documentation.
   

• Provide technical mentorship and guidance across the AI gineering organization.
• Support knowledge sharing, cross-training, and engineering excellence initiatives.
   

Technical Skills & Tools

Education

Bachelor's degree in Computer Science, Computer Engineering, Data Science, Information Systems, or equivalent. Master's degree preferred.

Required Knowledge, Skills & Abilities

• 4-6+ years of progressive data engineering experience with increasing scope and complexity.
• Deep expertise in Python, SQL, Scala, and distributed data systems (Apache Spark, Delta Lake).
• Strong experience with Azure Cloud, Databricks Lakehouse platform, Unity Catalog, and Databricks Workflows.
• Experience with Docker, Kubernetes, and CI/CD pipelines for data engineering workloads.
• Advanced monitoring and logging (Azure Monitor, Log Analytics, Databricks observability).
• Proven ability to design and deliver enterprise-scale data architectures (data lake, warehouse, lakehouse).
• Experience with data architectures supporting AI/ML workloads (e.g., vector databases, embedding pipelines, unstructured data processing).
• Strong data modeling skills (dimensional, data vault, lakehouse medallion patterns).
• Experience defining and enforcing data governance, quality, and security standards.
• Track record of mentoring engineers and establishing engineering best practices.
• Excellent communication skills with ability to influence technical decisions across teams.
  
   

Preferred Knowledge, Skills & Abilities

• Experience with Databricks Mosaic AI, Unity Catalog for AI governance, and Feature Store.
• Familiarity with RAG architectures, vector search (e.g., Pinecone, Weaviate, Azure AI Search), and embedding models.
• Experience with streaming data platforms (Kafka, Event Hubs, Spark Structured Streaming).
• Knowledge of LangChain or similar frameworks (understanding data requirements, not application development).
• Experience with graph databases and knowledge graph architectures.
• Infrastructure-as-code experience (Terraform, ARM templates, Bicep).
• Basic experience with Power BI or similar tools for semantic-layer-based data discovery.
• Familiarity with SAFe Agile or similar scaled agile frameworks.
• Experience operating in regulated environments with data compliance requirements.
  
  
   

Additional Information

• Must be able to work flexible hours/work schedule.
• Travel domestically and internationally as needed.
• Work holidays and weekends when required for production support or critical deliveries.
• Enjoys collaborative work and technical mentoring with peers and junior team members.
• A self-starter who thrives in ambiguous environments and drives clarity through technical leadership.
• This role is part of MBUSA's Data Insights & AI organization and contributes to the company's data and AI strategy and operating model.
   

EEO Statement

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.