Data Engineer (Starlink)

Summary

SpaceX is actively developing technologies to enable human life on Mars and is seeking a Data Engineer for the Starlink Enterprise team. The role focuses on improving data quality and building integrations to enhance decision-making processes and operational efficiency.

Responsibilities

• Improve data hygiene and quality across go-to-market systems — identify bad, missing, duplicate, and stale records; implement validation, enrichment, and remediation so higher-quality data enters our system of record, opportunity, partner/reseller, and enablement workflows
• Raise the quality of inputs to AI agents and analytics so outputs (recommendations, deal status, enablement answers, reporting) are more accurate, trustworthy, and actionable
• Build integrations across internal tools and external systems so deal, customer, and partner context flows cleanly between sales, channel ops, and operational platforms — reducing manual re-entry and preventing quality issues at the source
• Develop Model Context Protocol (MCP) servers and related interfaces that connect AI assistants (including Grok) to internal and external go-to-market systems, with an emphasis on reliable, well-scoped access to the right data and tools
• Design, ship, and iterate AI agents that power Starlink Enterprise go-to-market workflows
• Contribute to AI-powered applications and automations that accelerate lead-to-revenue cycles, working within the additional datasets and systems architecture owned by the team
• Instrument data quality and agent performance with monitoring and feedback loops — catching regressions early and tying improvements to outcomes (e.g., cleaner pipeline, faster cycle times, higher adoption and trust)
• Partner closely with sales, sales ops, channel/reseller ops, and enablement to turn recurring data-quality problems and manual processes into durable automations and standards
• Create reusable tools, checks, and documentation that help the team maintain data quality and extend agents over time

Skills

• Bachelor's degree in computer science, data science, physics, mathematics, or another STEM discipline
• 1+ years of professional data engineering or software engineering experience (internship experience applicable)
• 1+ years of development experience in Python or another modern programming language (SQL, etc.)
• Experience improving CRM data quality (deduping, enrichment, validation rules, field completeness, source-of-truth discipline)
• Experience building AI agents, LLM-powered applications, or tool-using assistants that integrate with real business systems (APIs, CRMs, ticketing, data warehouses)
• Experience designing APIs, webhooks, or integration layers that connect heterogeneous systems and keep data consistent across them
• Familiarity with Model Context Protocol (MCP) or similar patterns for exposing tools and data sources to AI systems
• Experience with CRM and go-to-market tooling; familiarity with Clay, HubSpot, Apollo, or similar sales engagement / enrichment platforms is a plus
• Professional experience supporting sales, sales ops, channel/reseller, or revenue operations workflows with data and automation
• Experience with SQL and modern data tooling; exposure to Grok, internal AI platforms, or comparable LLM developer tooling is a plus
• Experience with agent evaluation, guardrails, permissions, and human-in-the-loop patterns for production AI systems
• Ability to thrive in a fast-changing environment, take initiative on ambiguous problems, and develop new expertise as the product and stack evolve

Qualifications

Must Haves

• Bachelor's degree in computer science, data science, physics, mathematics, or another STEM discipline
• 1+ years of professional data engineering or software engineering experience (internship experience applicable)
• 1+ years of development experience in Python or another modern programming language (SQL, etc.)

Nice to Haves

• Experience improving CRM data quality (deduping, enrichment, validation rules, field completeness, source-of-truth discipline)
• Experience building AI agents, LLM-powered applications, or tool-using assistants that integrate with real business systems (APIs, CRMs, ticketing, data warehouses)
• Experience designing APIs, webhooks, or integration layers that connect heterogeneous systems and keep data consistent across them
• Familiarity with Model Context Protocol (MCP) or similar patterns for exposing tools and data sources to AI systems
• Experience with CRM and go-to-market tooling; familiarity with Clay, HubSpot, Apollo, or similar sales engagement / enrichment platforms is a plus
• Professional experience supporting sales, sales ops, channel/reseller, or revenue operations workflows with data and automation
• Experience with SQL and modern data tooling; exposure to Grok, internal AI platforms, or comparable LLM developer tooling is a plus
• Experience with agent evaluation, guardrails, permissions, and human-in-the-loop patterns for production AI systems
• Ability to thrive in a fast-changing environment, take initiative on ambiguous problems, and develop new expertise as the product and stack evolve