Prinicipal, Data Engineer

Mercedes-Benz USA, LLC

Mercedes-Benz USA, LLC Corporate Headquarters, Atlanta, GA

Job Type Full-time

Experience Senior

Posted 2 weeks 6 days ago

Senior Level

About Us

Mercedes-Benz USA is responsible for the marketing, sales, and service of Mercedes-Benz and Maybach products in the United States. We are looking for diverse, top-notch individuals to join the Mercedes-Benz team and uphold our corporate values.

Job Overview

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.

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.
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.

Data Platform & Product Delivery

Design and deliver scalable data platforms and data products supporting analytics, reporting, machine learning, AI, and enterprise decision-making.
Build and modernize end-to-end data pipelines across data lake, warehouse, lakehouse, data mart, and semantic consumption layers.
Enable engineers, analysts, data scientists, AI engineers, and business teams through reliable, governed, reusable data services.
Support batch, streaming, event-driven, and API-based data integration patterns.

Operational Excellence, Reliability & Governance

Identify and resolve systemic gaps in data quality, platform reliability, observability, performance, cost, resiliency, and operational readiness.
Establish best practices for production operations, monitoring, logging, incident response, runbooks, platform support, and continuous improvement.
Ensure platforms and data products comply with standards for security, governance, data quality, privacy, and responsible data use.
Drive automation through metadata management, reusable components, and repeatable engineering practices.

Collaboration, Influence & Technical Leadership

Partner with architects, infrastructure, security, analytics, AI/ML, product, and business stakeholders.
Define problem statements, success metrics, technical options, trade-offs, and implementation approaches.
Provide technical mentorship and guidance to engineers.
Lead cross-functional technical alignment and influence decisions without 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.
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 DevOps/DataOps practices.
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.
Familiarity with Power BI, Tableau, Qlik, or comparable platforms.
Experience with generative AI, agent-based solutions, vector databases, retrieval technologies, or enterprise AI platforms.
Experience in large-scale enterprise environments with multiple business domains and partner teams.

Qualifications

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 disciplines.
Experience designing, building, and operating enterprise-scale data platforms, data products, or shared engineering capabilities.
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, sound judgment, and mentoring ability.

Additional Information

Must be able to work flexible hours and schedules.
Travel domestically and internationally as needed.
Work holidays and weekends when required.
Collaborate with business, technology, and external partner teams across multiple time zones.

EEO Statement

Mercedes-Benz USA is committed to fostering an inclusive environment that appreciates and leverages the diversity of its team and provides equal employment opportunity to all qualified applicants and employees.