SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.

SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.

SR. ANALYTICS ENGINEER (X)

We are seeking a skilled Senior Analytics Engineer to build and maintain robust data systems that enable high-impact quantitative analysis and business decision-making. This role combines strong software engineering practices with expertise in large-scale data processing and advanced analytical methods to deliver reliable, scalable solutions across the organization. This is an opportunity to work on mission-critical systems that power quantitative decision-making at global scale.

RESPONSIBILITIES:

Design, implement, and optimize end-to-end data pipelines for processing high-volume datasets using tools such as Spark, Kafka, and/or Flink

Develop quantitative models and statistical frameworks to support experimentation, forecasting, and performance measurement

Build and maintain data infrastructure that ensures data quality, consistency, and accessibility for analytical workflows

Collaborate with product engineering, product, and operations teams to translate business requirements into production-grade data systems and insights

Conduct A/B tests, causal analysis, and performance evaluations to drive measurable improvements in key metrics

Implement monitoring, alerting, and automation for data systems to support real-time decision support

Mentor team members on best practices for scalable data engineering and quantitative problem-solving

BASIC QUALIFICATIONS:

Bachelor's or master's degree in computer science, statistics, applied mathematics, or quantitative field

5+ years of experience building production data pipelines and infrastructure at scale

Experience in Python, SQL, and distributed computing frameworks (e.g., Spark, Flink, Hadoop)

PREFERRED SKILLS AND EXPERIENCE:

Demonstrated expertise in statistical methods, predictive modeling, hypothesis testing, and experimental design