Principle Platform Engineer

HARMANs engineers and designers are creative purposeful and agile. As part of this team youll combine your technical expertise with innovative ideas to help drive cutting-edge solutions in the car enterprise and connected ecosystem. Every day you will push the boundaries of creative design and HARMAN is committed to providing you with the opportunities innovative technologies and resources to build a successful career.

A Career at HARMAN

As a technology leader that is rapidly on the move HARMAN is filled with people who are focused on making life better. Innovation inclusivity and teamwork are a part of our DNA. When you add that to the challenges we take on and solve together youll discover that at HARMAN you can grow make a difference and be proud of the work you do everyday.

About the Role

As a Senior/Principal Platform Engineer you will serve as the technical authority for Harmans BI AI and Data platforms owning both the architecture and hands-on development of scalable data pipelines platform components and infrastructure that enable reliable analytics and AI capabilities.

You will operate at the intersection of architecture and execution personally designing and building complex SQL and Python-based data pipelines while setting the technical direction for the broader platform. You will define architectural patterns drive engineering excellence and remain deeply embedded in the codebase writing production-grade code that sets the standard for the team.

This role requires exceptional technical depth architectural vision and the ability to lead through both design authority and hands-on contribution. You will shape platform strategy drive cross-team alignment and ensure the long-term scalability reliability and evolution of the data platform.

What You Will Do

1. Platform Architecture & Technical Authority

• Architectural Ownership: Define and own the end-to-end architecture of data pipelines platform services and infrastructure making binding technical decisions on design tooling and patterns.

• Hands-On Development: Personally architect and build complex SQL and Python-based data pipelines writing production-grade code that serves as the reference implementation for the team.

• Technical Vision: Set the long-term technical direction for the platform evaluating emerging technologies and defining the roadmap for platform evolution.

2. Data Pipeline Development & Engineering Excellence

• Pipeline Architecture & Development: Design architect and personally build high-complexity data pipelines for ingestion transformation and processing at scale using Python PySpark and SQL as primary development languages.

• SQL & Transformation Development: Author advanced SQL transformation logic complex query patterns and performant data models aligned with enterprise data architecture. Establish SQL development standards and reusable patterns for the team.

• Python & Framework Development: Build and maintain Python-based pipeline frameworks utilities and reusable components that accelerate team delivery and enforce consistency.

• Code Quality & Standards: Define and enforce coding standards for SQL and Python development across the team. Conduct deep code reviews and establish patterns for testability maintainability and performance.

• Performance Engineering: Lead root-cause analysis and resolution of complex pipeline failures performance bottlenecks and data inconsistencies applying deep expertise in query optimization and distributed processing.

3. Data Platform & Infrastructure Engineering

• Data Architecture Ownership: Architect and enforce structured data layers (e.g. Bronze/Silver/Gold) defining schemas data contracts and quality gates that ensure scalability and governance.

• Integration Architecture & Development: Design and build complex integrations with internal and external data sources including APIs streaming pipelines and event-driven architectures.

• Infrastructure as Code Leadership: Architect and implement Infrastructure as Code strategies using best-in-class tools defining patterns and modules that enable repeatable scalable provisioning.

• Environment Strategy: Own the strategy for environment consistency and reliability across Dev QA and Production designing promotion workflows and environment parity standards.

4. Deployment DevOps & Infrastructure as Code

• Release Strategy: Define and own the deployment strategy for data pipelines infrastructure and platform components across all environments.

• Infrastructure as Code Architecture: Architect enterprise-grade infrastructure provisioning using tools such as Terraform Pulumi or similar defining module structures state management and governance patterns.

• Cloud & Platform Architecture: Drive architectural decisions across modern cloud platforms (AWS Azure Databricks) designing scalable cost-effective and secure data solutions.

• CI/CD Architecture: Architect end-to-end CI/CD pipelines for automated testing validation and deployment of both application code and infrastructure.

• Source Control & Engineering Practices: Define and enforce Git workflows branching strategies code review practices and version control standards across the team.

5. Technical Leadership Standards & Mentorship

• Engineering Leadership: Provide deep technical mentorship to Senior and Junior Platform Engineers elevating team capabilities through pairing design reviews and hands-on guidance.

• Standards & Governance: Define and enforce coding standards architectural guidelines data governance policies and engineering best practices across the platform.

• Cross-Team & Stakeholder Alignment: Partner with System Architects Engineering Managers and senior stakeholders to align platform implementation with enterprise strategy and business objectives.

• Documentation & Knowledge Sharing: Drive creation of architectural decision records design documents and comprehensive technical documentation that enables team autonomy.

6. AI-Assisted Development & Platform Evolution

• AI-Driven Engineering: Leverage and champion AI-assisted tools to accelerate development debugging testing and documentation across the team.

• Platform Innovation: Identify and drive opportunities to improve platform efficiency through automation reusable frameworks and self-service capabilities.

• Continuous Improvement & Industry Leadership: Stay at the forefront of modern data platform AI and engineering trends evaluating and introducing technologies that advance platform maturity.

What Success Looks Like

• Platform architecture is well-defined scalable and consistently implemented across all components

• Complex SQL and Python pipelines you build serve as reference implementations and team standards

• Architectural decisions accelerate team delivery and reduce technical debt

• Complex issues are resolved permanently through architectural fixes not workarounds

• Data flows efficiently through well-governed well-structured architecture layers

• Infrastructure is robust scalable and fully automated through Infrastructure as Code (IaC)

• CI/CD and infrastructure processes are architected for reliability speed and auditability

• Senior and Junior engineers grow under your mentorship and adopt your engineering standards

• Platform capabilities continuously evolve in performance maturity and self-service enablement

What You Need to Be Successful

• Expert-level SQL development skills capable of authoring complex transformations performance-tuning queries and defining SQL development standards for the team

• Expert-level Python development skills capable of building production-grade pipeline frameworks utilities and data processing logic

• Deep experience with PostgreSQL relational databases and SQL-based transformation logic

• Extensive experience architecting and building data pipelines ETL/ELT and data processing workflows end-to-end

• Experience architecting implementing and governing Infrastructure as Code using tools such as Terraform Pulumi ARM/Bicep CloudFormation or similar

• Deep experience with PySpark Spark Databricks or distributed data processing at scale

• Extensive hands-on experience with cloud platforms (AWS Azure Databricks) at an architectural level

• Proven ability to design implement and govern CI/CD pipelines and DevOps practices at enterprise scale

• Strong experience with Git workflows branching strategies and source control governance

• Advanced troubleshooting performance engineering and optimization skills

• Deep experience designing building and troubleshooting APIs and complex data integration patterns

• Experience designing highly available fault-tolerant distributed systems at enterprise scale

• Strong understanding of data governance metadata management lineage and data quality architectures

• Experience with streaming and event-driven architectures

• Demonstrated ability to mentor lead and elevate engineering teams through technical authority and hands-on contribution

Bonus Points if You Have

• Experience architecting enterprise-scale Lakehouse platforms using Databricks Delta Lake Unity Catalog and Delta Live Tables (DLT)

• Experience with real-time and event-driven data architectures using Kafka Event Hubs Kinesis or similar streaming technologies

• Experience implementing data observability lineage and monitoring platforms (e.g. Monte Carlo Datafold OpenLineage Unity Catalog)

• Experience designing and enforcing DataOps practices data contracts and domain-oriented data ownership models

• Experience with containerization and orchestration technologies such as Docker and Kubernetes

• Experience building platform engineering capabilities including self-service infrastructure reusable platform components and internal developer platforms

• Experience implementing enterprise security architectures including RBAC secrets management encryption and zero-trust principles

• Experience optimizing cloud cost performance and capacity planning for large-scale data platforms

• Experience supporting AI/ML platforms feature stores model deployment infrastructure or MLOps tooling

• Experience with modern software engineering practices including test automation platform reliability engineering and Site Reliability Engineering (SRE) principles

• Experience with metadata management data catalogs lineage and governance frameworks

• Experience leading large-scale platform modernization cloud migration or data platform transformation initiatives

• Experience working in manufacturing automotive supply chain IoT or other data-intensive enterprise environments

• Contributions to open-source projects technical publications conference presentations or recognized industry thought leadership

• Bachelors degree in Computer Science Software Engineering Information Systems or related field; Masters degree preferred

What we offer

• Work in international teams with the worlds largest automotive manufacturers.

• Use HARMAN Flex choose the right working model fitting their job (Hybrid or Office)

• Access career development opportunities and get both on the job training on high-end automotive projects as well as extensive technical trainings.

• Access HARMAN Webshop with large employee discounts for audio products (JBL HARMAN Kardon AKG)

• Are encouraged to a healthy work/life balance with a progressive holiday plan based on total work experience 2 sick days 3 days off compensation for weekend legal holidays and flextime.

• And of course enjoy meal tickets a premium medical subscription fitness discounts and other corporate perks.

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HARMAN is proud to be an Equal Opportunity / Affirmative Action employer. All qualified applicants will receive consideration for employment without regard torace religion color national origin gender (including pregnancy childbirth or related medical conditions) sexual orientation gender identity gender expression age status as a protected veteran status as an individual with a disability or other applicable legally protected characteristics.