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
HARMAN’s engineers and designers are creative, purposeful and agile. As part of this team, you’ll 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, you’ll discover that at HARMAN you can grow, make a difference and be proud of the work you do everyday.
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