Data Engineer – Audio Data Platform

About the role

As a Data Engineer on the Innovation Team at HARMAN Automotive, you will build an end‑to‑end audio data collection and consumption platform that powers advanced analytics, audio information retrieval, and personalization across the Car Audio Division. You will work closely with Data Scientists, ML Engineers, Embedded/DSP Engineers, and Audio Experts to deliver high‑quality data products while meeting strict privacy, security, and automotive compliance requirements.

Key responsibilities

• Design and implement standardized data ingestion frameworks for batch and streaming sources (internal databases, user‑preference data, vehicle telemetry).
• Define and maintain data models and contracts (schemas, semantics, versioning) for audio‑related files, user preferences, and metadata, including validation and anomaly detection.
• Develop and maintain data lake / lakehouse architectures.
• Design and implement a standardized API for data consumption.
• Prepare and curate high‑quality datasets for AI/ML model training, validation, experimentation, and statistical analysis.
• Collaborate with ML Engineers and Data Scientists to optimise data formats for training performance and storage efficiency.
• Design cost‑aware policies for data retention, sampling, compression, and technology selection.
• Optimise pipeline execution times and resource usage (batch vs streaming, compute sizing, caching strategies).
• Establish measurable KPIs for data cost efficiency, pipeline reliability, and performance.
• Ensure compliance with internal policies, OEM requirements, and regulatory constraints.
• Create clear documentation for pipelines, schemas, and architectural decisions.
• Mentor other engineers on practical data engineering, performance tuning, and cost‑efficient design.

Required profile

• Strong execution mindset with the ability to design, implement, debug, and optimise data pipelines.
• Experience delivering scalable, secure, and compliant data solutions in a complex engineering environment.
• Ability to collaborate effectively with cross‑functional teams including data scientists, ML engineers, and embedded engineers.

Required skills

• Data ingestion (batch and streaming)
• Data modeling and contract management
• Data lake / lakehouse architecture
• API development for data consumption
• Schema validation and anomaly detection
• Cost‑aware data retention and compression strategies
• Pipeline performance optimisation
• KPI definition and monitoring