Sr. Software Engineer, Data Engineering (X)

SpaceXSI•Palo Alto, CA

14h•$180,000 - $330,000

About The Position

As a Data Engineer on the X product engineering team, you will provide comprehensive data solutions for a wide range of stakeholders, including end users and internal Product Engineering, Algorithm, Legal, Finance, and Sales teams. The work uses AI, distributed computing, and hybrid storage technologies, and also addresses broader engineering challenges to maximize the value of data for users. 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 through 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, as well as 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 RDBMS and NoSQL.
Experience solving large-scale problems and doing incremental quality work while building new systems that 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 to ingest, process, validate, and deliver data for user-behavior insights and product decisions.
Create shared datasets, fact tables, and internal data products that enable teams to analyze, debug, and improve product performance.
Prototype and build tools to automate and accelerate internal data workflows, including backfills, dashboards, report generation, and self-service data access.
Own data correctness end to end through output invariants, denominator reconciliation, and independent recomputation; lead root-cause investigations when key metrics change unexpectedly.
Work across query engines and frameworks, selecting appropriate tools and adapting to new infrastructure and environments.
Partner with product and business teams to identify data gaps and prioritize high-impact opportunities for data acquisition and improvement.
Iterate quickly on feedback and ship useful increments, prioritizing efficient, accurate, and reliable solutions.