Top Data Engineer Jobs in Austin, TX

As a Data Architect at PwC, you will design and implement data architecture strategies, develop data pipelines, and integrate data solutions while collaborating with stakeholders. You'll be focused on transforming raw data into actionable insights and ensuring compliance with governance and security policies.

The Senior Data Engineer will build and maintain data lakes and pipelines, improve data ingestion efficiency, and enhance self-serve data engineering capabilities. They will work with stakeholders to design and architect scalable solutions while ensuring high data quality and applying strong technical experience in data services management.

The Database SRE will design, build, and maintain HRT's SQL and NoSQL database infrastructure, focusing on performance and scalability. Responsibilities include configuring PostgreSQL and MySQL servers, working with infrastructure-as-code tools, and optimizing database performance with various monitoring tools.

As a Senior Data Engineer, you will focus on building data infrastructure, managing ETL pipelines, collaborating with analysts, maintaining databases, and implementing process improvements. You will lead the development of data services and stay updated with cloud technologies to enhance data handling across the company.

The Data Center Selection Manager will enhance the quality and cost of colocation sites while managing vendor relations and executing contracts. Responsibilities include defining vendor strategy, conducting negotiations, working cross-functionally, and managing multiple projects effectively.

As a Data Specialist, you will design and develop analytics domains, collaborate with data teams, own data visualization tools, manage the data catalog, and provide insights based on product data, contributing to data-driven decisions within Sonar. You will work closely with stakeholders to define analytics needs and ensure metrics are accessible and well-defined.

As a Senior AI Engineer, you will design, implement, and maintain large-scale AI/ML pipelines, focusing on training and tuning deep learning models and evaluating large language models while working on data collection and feature engineering.