Prinicipal, Data Engineer

Mercedes-Benz USA•Atlanta, GA

About The Position

Mercedes-Benz USA is seeking a Principal Data Engineer to serve as a senior technical leader for enterprise data engineering. The role defines complex problem spaces, sets architectural direction, and delivers scalable, enterprise-grade data platforms and products for reporting, analytics, machine learning, AI products, and digital business capabilities. The Principal Data Engineer owns outcomes and business impact in high-ambiguity environments and establishes standards, frameworks, and reusable engineering patterns used across multiple teams and domains. The position defines enterprise data engineering architecture, design patterns, standards, and best practices; influences technology choices; and evaluates technologies to improve scalability, performance, security, cost efficiency, and developer productivity. It designs and delivers data platforms and end-to-end pipelines across lake, warehouse, lakehouse, data mart, and semantic layers, supporting batch, streaming, event-driven, and API-based integration. The role drives operational excellence, reliability, observability, data quality, governance, security, privacy, and responsible data use. It partners with architecture, infrastructure, security, analytics, AI/ML, product, business, and external partner teams to turn complex needs into technical solutions. It also provides technical mentorship, leads cross-functional alignment, and influences engineering decisions without formal reporting authority. Flexible hours may be required, as may domestic and international travel and work on weekends or holidays. The role collaborates with teams across multiple time zones.

Requirements

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.
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.
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 a strong ownership mindset, sound judgment, and the ability to mentor engineers and influence engineering direction.
Ability to work flexible hours or schedules.
Ability to travel domestically and internationally as needed.
Ability to work weekends and holidays when required.

Nice To Haves

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.

Responsibilities

Define and evolve enterprise data engineering architecture, design patterns, standards, and best practices across platforms and products.
Create reusable engineering frameworks, templates, automation standards, and playbooks to accelerate delivery and improve consistency.
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 to improve scalability, performance, security, cost efficiency, and developer productivity.
Design and deliver scalable, high-performance data platforms and products for analytics, reporting, machine learning, AI, and enterprise decision-making.
Build and modernize end-to-end pipelines across data lake, warehouse, lakehouse, data mart, and semantic consumption layers.
Enable engineers, analysts, data scientists, AI engineers, and business teams with reliable, governed, reusable data services.
Support batch, streaming, event-driven, and API-based data integration capabilities.
Identify systemic gaps in data quality, reliability, observability, performance, cost, resiliency, and operational readiness, and drive solutions through completion.
Establish production operations practices for monitoring, logging, incident response, runbooks, platform support, and continuous improvement.
Ensure platforms and products meet enterprise standards for security, governance, data quality, privacy, and responsible data use.
Automate engineering through metadata management, reusable components, and repeatable practices to reduce manual effort and operational risk.
Partner with architects, infrastructure, security, analytics, AI/ML, product, business, and external partner teams to translate business needs into scalable technical solutions.
Define problem statements, success metrics, technical options, trade-offs, and implementation approaches in ambiguous situations.
Mentor and guide engineers to improve data engineering maturity and engineering excellence.
Lead cross-functional technical alignment and influence decisions without formal reporting authority.
Travel domestically and internationally as needed; work flexible hours and weekends or holidays when required.
Collaborate with business, technology, and external partner teams across multiple time zones.