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%)
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Define and drive enterprise data engineering architecture, standards, and best practices across the organization.
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Design and deliver scalable, high-performance data platforms supporting analytics, machine learning, and generative AI use cases.
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Architect data lake, warehouse, and lakehouse solutions using Databricks, Delta Lake, and Unity Catalog.
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Design and deliver data platform capabilities that support generative AI and agent-based workloads, including embedding pipelines, vector store integration, and knowledge base management.
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Identify systemic gaps in data quality, reliability, performance, and cost, and drive solutions end-to-end.
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Establish reusable frameworks, patterns, and playbooks for data pipeline development adopted across teams.
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Define data partitioning, optimization, and caching strategies for high-volume and low-latency workloads.
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Lead technical design reviews and architecture decision records (ADRs) for major data platform initiatives.
AI/GenAI Data Infrastructure (20%)
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Design and implement data architectures that serve AI/ML model training, RAG pipelines, and agent-based systems.
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Build and govern embedding pipelines, vector database ingestion workflows, and knowledge base refresh processes.
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Define data quality, freshness, and governance standards for AI-consumed datasets.
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Architect unstructured data processing pipelines (document parsing, chunking strategies, metadata enrichment) for GenAI consumption.
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Collaborate with LLMOps and AI Solutions engineers to define data contracts and integration patterns between data platform and AI systems.
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Evaluate and integrate emerging data technologies that support AI workloads (vector databases, graph databases, semantic search).
Technical Leadership & Strategy (20%)
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Operate in high-ambiguity environments by defining problem statements, success metrics, and technical approach.
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Influence technology decisions and contribute to organizational data and platform strategy.
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Mentor and coach data engineers across the team, establishing technical growth paths and skill development.
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Drive adoption of engineering best practices including code review standards, testing frameworks, and CI/CD maturity.
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Represent data engineering in cross-functional planning with AI engineering, analytics, and business teams.
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Evaluate emerging technologies, frameworks, and engineering approaches for potential adoption.
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Contribute to enterprise-wide data strategy, platform roadmap, and architecture governance discussions.
Operational Excellence & Governance (10%)
Collaboration & Stakeholder Engagement (10%)
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:
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Designing data models and pipeline architectures for new business requirements, drawing architecture diagrams and writing technical design documents.
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Writing and reviewing complex data transformation code in Databricks (Python, Spark, SQL) for high-volume and high-complexity use cases.
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Leading technical design sessions with the team to solve ambiguous data challenges and establish patterns for reuse.
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Reviewing pull requests and providing architectural guidance and mentorship to team members.
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Meeting with AI engineering teams (LLMOps, AI Solutions) to align on data contracts, embedding pipeline requirements, and vector store integration.
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Investigating and resolving complex production issues including performance bottlenecks, data quality degradations, and pipeline failures.
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Evaluating new tools and technologies (e.g., vector databases, streaming frameworks, governance tooling) through proof-of-concepts.
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Participating in architecture review boards and contributing to enterprise data strategy discussions.
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Defining and tracking data platform KPIs (pipeline reliability, data freshness, query performance, cost efficiency).
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Conducting 1:1 mentoring sessions with junior and mid-level data engineers on technical growth.
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Collaborating with data governance and security teams on access policies, data classification, and compliance requirements.
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Writing and maintaining engineering playbooks, architectural decision records, and onboarding documentation.
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Provide technical mentorship and guidance across the AI gineering organization.
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Support knowledge sharing, cross-training, and engineering excellence initiatives.
