About the Role
As a Data Engineer, you will serve as the critical link between global business automotive operations and scalable data platform execution. Your primary focus will be translating complex operational needs into robust, reusable data pipelines that power analytics, BI, and AI use cases.
You will act as a full-stack data engineer, working across the lifecycle of data—ingestion, transformation, modeling, and delivery. Unlike role-specific analytics development, your solutions will serve multiple downstream consumers, requiring a strong focus on scalability, reliability, and reusability.
This role requires a hybrid mindset: deep understanding of business processes in Manufacturing, Procurement, and SCM, combined with strong engineering discipline to build high-performance, production-grade data pipelines.
1. Business Analysis & Requirements Translation
- Stakeholder Partnership:
- Work with business users, analysts, and developers to understand data requirements across multiple use cases.
- Technical Translation:
- Translate business needs into scalable data models, pipeline logic, and data integration requirements.
- Process Alignment:
- Develop a deep understanding of operational workflows to ensure that data pipelines accurately represent business processes and can support diverse analytical needs.
2. Data Pipeline Design & Development
- Pipeline Design:
- Design and implement scalable data pipelines for ingestion, transformation, and delivery of data across multiple domains.
- Reusability & Scalability:
- Build pipelines that serve multiple consumers (BI, AI, data products), ensuring flexibility and extensibility.
- Performance Optimization:
- Optimize data processing for large-scale datasets, ensuring efficient and reliable execution.
3. Data Engineering & Modeling
- SQL & Data Transformation:
- Write efficient SQL queries, transformations, and data processing logic to structure data for downstream consumption.
- Data Modeling:
- Design and maintain data models aligned with enterprise standards (e.g., layered/medallion architecture).
- Data Product Enablement:
- Develop and contribute to shared, governed data products that act as single sources of truth across the organization.
- Data Quality:
- Implement validation, testing, and monitoring to ensure data accuracy, consistency, and reliability at scale.
4. Deployment & Lifecycle Management
- Promotion & Release:
- Manage deployment of pipelines and data solutions across Dev → QA → Production environments, ensuring stability and reliability.
- Source Control:
- Use Git to manage pipeline code, configurations, and versioning following team standards.
- Monitoring & Maintenance:
- Monitor pipeline performance, troubleshoot failures, and continuously improve reliability and efficiency.
5. Collaboration & Standards
- Team Alignment:
- Work closely with the System Architect to ensure pipelines align with architectural patterns, scalability requirements, and security standards.
- Cross-Functional Collaboration:
- Enable downstream teams (BI, AI, analytics) by delivering high-quality, well-structured data.
- Peer Review:
- Participate in code reviews to ensure consistency, maintainability, and adherence to best practices.
6. AI-Assisted Development & Automation
- AI-Assisted Coding:
- Use AI tools to accelerate development of data pipelines, transformations, and documentation.
- Automation:
- Automate repetitive data engineering tasks such as data validation, transformation generation, and pipeline monitoring.
- Continuous Upskilling:
- Develop expertise in modern data engineering practices, including distributed processing and AI-enabled workflows.
What Success Looks Like
- Data pipelines are robust, scalable, and reusable across multiple use cases
- Data products provide consistent, trusted, and governed data
- Systems handle large volumes of data efficiently and reliably
- Downstream teams (BI, AI) can build faster using high-quality data foundations
- Data issues are proactively identified and resolved
What You Need to Be Successful
- Strong SQL and data transformation skills
- Experience with data pipelines and ETL/ELT frameworks
- Understanding of data modeling and layered architectures (e.g., Medallion)
- Experience with large-scale data processing (e.g., Spark, Databricks) is a plus
- Familiarity with cloud platforms (AWS, Azure) is a plus
- Experience with Git and modern development practices
- Strong problem-solving and performance optimization skills
- Understanding of APIs and data integration patterns
- Working knowledge of Python for data processing, automation, and pipeline development
Bonus Points if You Have
- Experience with Databricks, Spark, or distributed data processing frameworks
- Experience working with cloud platforms such as Azure or AWS
- Experience building data pipelines using Python
- Familiarity with Delta Lake, Medallion Architecture, or modern Lakehouse concepts
- Experience working with manufacturing, supply chain, procurement, or operational data
- Exposure to BI and analytics tools such as Power BI, Qlik, Tableau, or similar platforms
- Familiarity with REST APIs, JSON, and integrating external data sources
- Experience using AI-assisted development tools such as GitHub Copilot, ChatGPT, or similar coding assistants
- Understanding of data quality, validation, and testing concepts
- Experience with Agile development methodologies and collaborative software development practices
- Bachelor's degree in Computer Science, Information Systems, Engineering, Data Analytics, or a related field
HARMAN is proud to be an Equal Opportunity / Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, 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.
Skills Required
- Strong SQL and data transformation skills.
- Experience with data pipelines and ETL/ELT frameworks.
- Understanding of data modeling and layered architectures (e.g., Medallion).
- Experience with Git and modern development practices.
- Strong problem-solving and performance optimization skills.
- Understanding of APIs and data integration patterns.
- Working knowledge of Python for data processing, automation, and pipeline development.
- Experience with large-scale data processing (e.g., Spark, Databricks).
- Familiarity with cloud platforms (AWS, Azure).
- Familiarity with Delta Lake, Medallion Architecture, or modern Lakehouse concepts.
- Experience working with manufacturing, supply chain, procurement, or operational data.
- Exposure to BI and analytics tools (Power BI, Qlik, Tableau).
- Familiarity with REST APIs and JSON.
- Experience using AI-assisted development tools (GitHub Copilot, ChatGPT) and automation.
- Understanding of data quality, validation, and testing concepts.
- Experience with Agile development methodologies and collaborative software development practices.
- Bachelor's degree in Computer Science, Information Systems, Engineering, Data Analytics, or related field.
Harman Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Harman and has not been reviewed or approved by Harman.
-
Healthcare Strength — Healthcare coverage is described as comprehensive, often including medical, dental, and vision plans, with HSAs/FSAs and disability or life insurance also referenced. Wellbeing support such as EAP access and onsite occupational health services is also part of the package in some regions.
-
Wellbeing & Lifestyle Benefits — Lifestyle-oriented benefits include employee product discounts and region-specific perks like free fruit deliveries, Cycle to Work schemes, and meal coupons or allowances. Flexible schedules and work-from-home options are also part of the overall offering where roles and projects allow.
-
Fair & Transparent Compensation — Pay is repeatedly characterized as solid or market-aligned for many roles, with salary payments described as consistent and on time. Compensation is also tied to performance in some accounts, reinforcing a perception of basic fairness for a portion of employees.
Harman Insights
What We Do
Headquartered in Stamford, Connecticut, HARMAN (harman.com) designs and engineers connected products and solutions for automakers, consumers, and enterprises worldwide, including connected car systems, audio and visual products, enterprise automation solutions; and services supporting the Internet of Things. With leading brands including AKG®, Harman Kardon®, Infinity®, JBL®, Lexicon®, Mark Levinson® and Revel®, HARMAN is admired by audiophiles, musicians and the entertainment venues where they perform around the world. More than 50 million automobiles on the road today are equipped with HARMAN audio and connected car systems. Our software services power billions of mobile devices and systems that are connected, integrated and secure across all platforms, from work and home to car and mobile. HARMAN has a workforce of approximately 30,000 people across the Americas, Europe, and Asia. In March 2017, HARMAN became a wholly-owned subsidiary of Samsung Electronics Co., Ltd. HARMAN is an Equal Opportunity, Affirmative Action employer. Minorities, women, veterans and individuals with disabilities are encouraged to apply. HARMAN offers a great work environment, challenging career opportunities, professional training and competitive compensation. Looking for a challenge where your experience is valued? Come see what you can achieve as a leader with HARMAN!
