Data Engineer

Data Engineer

Location: Austin, TX

Contract

Job Description:

The Data Engineer designs, builds, and manages scalable AI and data platforms and pipelines using AI tools, enabling the organization to collect, process, analyze, and leverage data efficiently for business insights. This role combines hands-on data engineering expertise with strong system design capabilities to support enterprise-wide data and AI initiatives.

Job Overview:

The Data Engineer will lead the design and development of modern data solutions, including data warehouses, data lakes, ETL/ELT pipelines, cloud-based analytics platforms, and machine learning data infrastructure. The successful candidate will collaborate closely with semiconductor engineers, data scientists, business stakeholders, and IT teams to deliver reliable, secure, and scalable data capabilities. This position requires deep expertise in data architecture, data integration, cloud technologies, and software engineering best practices, as well as the ability to mentor team members, influence technical direction, and drive continuous improvement across the data ecosystem.

- 8-10 years of experience in system design, database design, data modeling, and data architecture preferably within the - semiconductor industry or related high-tech sectors

- Hands-on experience with building enterprise software with AI coding tools (example cursor, Claude code), a variety of ETL and database/warehousing/integration tools like Snowflake, Databricks, NiFi & Postgres.

- Proficiency in SQL & Python.

- At least 5+ years’ experience with cloud computing platforms such as AWS, Azure, or Google Cloud Platform is highly desirable (Azure is preferred).

- Experience working within semiconductor industry or related high-tech sectors

- Experience collaborating with international teams, particularly in China and India.

- Experience with Infrastructure-As-Code and CI/CD automation

- Strong communication skills and the ability to collaborate effectively with semi-conductor engineers, data scientists, and business stakeholders