Sr. Software Engineer, Data Engineering (X)

xAI•Palo Alto, CA

About The Position

As a Data Engineer on the X product engineering team, you will provide comprehensive data solutions for end users and internal stakeholders, including Product Engineering, Algorithm, Legal, Finance, and Sales. The work uses AI, distributed computing, and hybrid storage technologies, and also addresses broader engineering challenges to maximize the value of data for a diverse user base. You will help build and operate a distributed data platform that powers hundreds of real-time and batch pipelines processing billions of events per day. The team operates like an internal startup, owning problems end to end—from raw event streams to the datasets, tools, and metrics that product, growth, safety, and business teams rely on daily.

Requirements

At least 5 years of professional software engineering experience.
Experience with Python, Rust, Scala, Go, or Java.
Experience with data pipeline tooling and distributed systems.

Nice To Haves

Software engineering experience in data engineering or distributed systems.
Knowledge of real-time and batch data processing tools such as Spark, Kafka, Flink, and SQL, and of storage systems in relational databases or NoSQL.
Experience solving large-scale problems and doing incremental quality work while building new systems to enable future quality improvements.
A proven record of translating product requirements into engineering implementation plans and communicating effectively with AI, product, marketing/sales, and engineering groups.

Responsibilities

Design, build, and operate production-grade real-time and batch pipelines that ingest, process, validate, and deliver data for user-behavior insights and product decisions.
Create shared datasets, fact tables, and internal data products to help teams analyze, debug, and improve product performance.
Prototype and build tooling to automate and accelerate internal data workflows, including backfills, dashboards, report generation, and self-serve data access.
Own data correctness end to end by validating output invariants, reconciling denominators, independently recomputing results, and leading root-cause investigations when key metrics move unexpectedly.
Work across query engines and frameworks, choosing appropriate tools and adapting to new infrastructure and environments.
Partner with product and business teams to identify data gaps and prioritize high-impact data acquisition and improvement opportunities.
Iterate quickly on feedback and ship useful increments with a focus on efficient, accurate, and reliable solutions.