Senior Data Engineer, GTM

Senior Data Engineer, GTM corporate_fare Google place Mountain View, CA, USA ; Chicago, IL, USA ; +3 more ; +2 more bar_chart Mid Mid Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area. info_outline X Applicants in the

County of Los Angeles:

Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.

Note:

By applying to this position you will have an opportunity to your preferred working location from the following: Mountain View, CA, USA; Chicago, IL, USA; New York, NY, USA; Irvine, CA, USA .

Minimum qualifications:

Bachelor's degree or equivalent practical experience. 5 years of experience coding in Python and SQL. 5 years of experience working with machine learning operations (MLOps) and large language model operations (LLMOps) principles and data infrastructure, including deploying text processing and embedding pipelines. 5 years of experience designing and deploying data pipelines, including managing data schemas and processing unstructured text data for machine learning (ML) workflows.

Preferred qualifications:

Experience with data schemas. Experience with google colaboratory (Colab), TensorFlow, Tensor Processing Units (TPUs), and agentic tools and platforms for processing unstructured text data. Experience with LLM orchestration and agentic infrastructure. Proficiency in SQL and Python. Understanding of MLOps/LLMOps principles to ensure the scalable and reliable deployment of text processing and embedding pipelines. About the job $156000 - $227000 (USD) + 15% bonus target + bonus + equity + benefits. Learn more about . Responsibilities Design and maintain pipelines to ingest, clean, and process massive volumes of unstructured data, including business transcripts and support cases, into reliable analytical datasets. Architect and deploy advanced platforms and tooling that empower the team to leverage autonomous AI agents and Large Language Models (LLMs) for intelligent routing and automated insights. Develop internal libraries and self-serve frameworks that streamline Natural Language Processing (NLP) and causal analysis, significantly reducing operational friction and enhancing team productivity. Manage and optimize embedding workflows using TensorFlow and Tensor Processing Units (TPUs), ensuring efficient processing that bypasses standard API constraints for high-volume data. Implement automated monitoring, alerting, and rigorous data quality checks to guarantee the security, reliability, and governance of high-stakes analytical assets.