Data Engineer

About the job:
The Data and Analytics Services (DAS) team is a data hub for the Platforms and Devices (P&D) organization. Our mission is to empower the devices organization with timely, accurate, and actionable data. We build and manage the centralized data warehouse, creating data pipelines and scalable analytics solutions on Google Cloud Platform. By partnering closely with business stakeholders, we translate complex data needs into tangible technical solutions that drive decision-making, product strategy, and operational efficiency via data driven insights. We are committed to upholding data quality, governance, and best practices, ensuring that data is a reliable and transformative asset for all Platforms and Devices.

Responsibilities:
Lead the design, development, and maintenance of data pipelines and Extract, Transform, and Load/Extract, Load and Transform (ETL/ELT) processes for the centralized data warehouse.
Architect and optimize SQL queries for data transformation, analytics, and reporting.
Develop and manage data foundations and models specifically designed to support Artificial Intelligence/Machine Learning (AI/ML) initiatives and the generation of AI-motivated insights. Develop and maintain data infrastructure to support the data foundations.
Partner with executive business stakeholders, data scientists, and Artificial Intelligence (AI) teams to understand requirements, architect data solutions.
Collaborate with other data engineers to deliver data solutions and promote technical growth within the team.

Minimum qualifications:
Bachelor’s degree in Computer Science, Engineering, Information Systems, a related quantitative field, or equivalent practical experience.
3 years of experience in a Data Engineering, Data Infrastructure, or Data Analytics role.
Experience with data engineering and writing software in Python or SQL.
Experience in managing and maintaining data projects from conception to production.
Experience building and maintaining data pipelines tailored for ML, AI, or advanced analytics workloads.

Preferred qualifications:
Experience supporting AI/ML initiatives and with Generative AI technologies, LLM integrations, RAG, Fine-tuning of models and vector databases.
Experience building and productionizing end-to-end data pipelines using ETL tools.
Experience with advanced data modeling and schema design for analytics.
Expertise with Google Cloud Platform (GCP) data services (e.g., BigQuery, Dataflow, Pub/Sub) and AI infrastructure (e.g., Vertex AI, BigQuery ML).
Familiarity with the consumer electronics industry or supply chain data.
Excellent stakeholder management and communication skills, with the ability to translate technical concepts to non-technical audiences.