Analytics Engineer: Build Scalable Data Pipelines & Models

SpaceXAI; San Jose (CA); On-site; USD 180,000 - 440,000; Full time

Benefits offered by this job

Equity

Medical, Vision, Dental

401(k) retirement plan

Disability insurance

Life insurance

Employee discounts

Job summary

SpaceXAI is seeking an Analytics Engineer to build and maintain data systems enabling high-impact quantitative analysis and business decisions. You will design end-to-end pipelines, develop models for experimentation, and ensure data quality across a scalable analytics platform.

You will work with Spark, Kafka, Flink, and cloud services to deliver reliable data systems. The role emphasizes hands-on engineering, collaboration with cross-functional teams, and mentorship for scalable data practices.

Qualifications

• 4+ years building production data pipelines and infrastructure at scale.
• Strong proficiency in Python, SQL, and distributed computing frameworks.
• Demonstrated expertise in statistical methods, predictive modeling, hypothesis testing, and experimental design.
• Solid understanding of cloud services for data storage, processing, and orchestration.

Responsibilities

• Design, implement, and optimize end-to-end data pipelines for high-volume datasets.
• Develop quantitative models and statistical frameworks for experimentation and forecasting.
• Build and maintain data infrastructure ensuring data quality and accessibility.
• Collaborate with product engineering, product, and operations teams to translate requirements into production data systems.

Skills

Python

SQL

Statistical modeling

Experiment design

Problem solving

Education

CS/Stats/Math degree

Tools

Spark

Flink

Hadoop

Job description

SpaceXAI is seeking an Analytics Engineer to build and maintain data systems enabling high-impact quantitative analysis and business decisions. You will design end-to-end pipelines, develop models for experimentation, and ensure data quality across a scalable analytics platform.

You will work with Spark, Kafka, Flink, and cloud services to deliver reliable data systems. The role emphasizes hands-on engineering, collaboration with cross-functional teams, and mentorship for scalable data practices.