Software Engineer - X Data

About the role

This position centers on data engineering responsibilities for the X product engineering organization. The hire will own the design, implementation, and maintenance of critical data pipelines that serve multiple internal stakeholders. You will collaborate closely with analysts, product managers, and other engineers to ensure data infrastructure meets evolving business requirements. A significant portion of the work involves translating ambiguous product needs into robust and scalable data solutions. You will be responsible for optimizing existing pipelines to improve reliability, performance, and cost efficiency. The role demands a proactive approach to identifying data quality issues and preventing them before they impact downstream users. You will play a key part in ensuring that the right data is available at the right time to support strategic decisions.

Key facts

What you'll do

These pipelines handle hundreds of events per day across diverse internal teams.

This work supports analytics, safety, growth, and business needs.

These tools reduce manual effort and speed up reliable data delivery.

Root-cause investigations are led when key metrics move unexpectedly.

Decisions balance tradeoffs between latency, scalability, and simplicity.

This alignment ensures the most valuable datasets are available.

Changes are driven by user-behavior insights and product decisions.

You will implement monitoring and alerting to ensure data pipeline health and timely detection of anomalies.

The role involves working with large datasets to build ETL processes that are efficient and maintainable.

You will partner with data scientists to ensure that data models support machine learning workflows and experiments.

The position requires writing clean documentation to help other engineers understand data schemas and pipeline logic.

You will evaluate new data technologies and propose improvements to the current stack based on industry best practices.

The job includes participating in on-call rotations to respond to data outages and service disruptions.

You will ensure that all data handling complies with internal policies and external regulatory standards.

Requirements

Professional software engineering experience of 3+ years is required, ideally in data engineering or distributed systems. Candidates must have hands-on expertise in Python, Rust, Scala, Go or Java, plus data pipeline tooling and distributed systems.

Knowledge of realtime and batch data processing tools such as Spark, Kafka, Flink, SQL, and various storage systems in relational and NoSQL databases is required. Experience with RMDBs and NoSQL systems must support reliable data platforms.

Experience solving large-scale problems is required, including comfort with incremental quality work while building brand new systems to enable future quality improvements.

Records of interpreting product requirements into engineering implementation plans must be demonstrated. Effective communication with AI, product, marketing/sales, and engineering groups is mandatory.

A Bachelor's degree or equivalent experience is required, with a focus on computer science or a related technical field.

You must be able to work in Palo Alto, California, or be willing to relocate for the position.

The role requires passing standard employment eligibility verification as required by law.

The position demands the ability to read, write, and understand technical specifications in English.

Practical notes

SpaceXAI operates as an equal opportunity employer. Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Data engineers work with distributed systems and streaming platforms to turn raw events into actionable information. SQL, Python, and modern data stacks power analytics and product decisions across tech companies.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.