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Senior Engineer, Analytics, Operations Intelligence
Location
California + 20 moreAll locations: California | Colorado | Connecticut | Florida | Illinois | Iowa | New Jersey | New Mexico | New York | North Carolina | Ohio | Maryland | Massachusetts | Michigan | Missouri | Pennsylvania | Tennessee | Texas | Utah | Virginia | Washington
Posted
23 days ago
Salary
$115.2K - $137.6K / year
Seniority
Senior
Job Description
Senior Engineer, Analytics, Operations Intelligence
Crisis Text Line
• Define and maintain performant, scalable data models in the data warehouse, with a focus on reliability, reusability, and documentation • Partner with Platform and Data Engineering to build and monitor scalable data pipelines in production • Serve as a technical authority on data modeling standards, tooling choices, and engineering best practices across the analytics function • Translate ambiguous business needs into clean, well-scoped data products — from one-time analyses to production-grade dashboards • Advocate for and drive adoption of industry best practices • Define the organization's experimentation standards: what constitutes a well-designed study, how results are interpreted, and how findings are documented and shared • Design and execute experiments — hypothesis testing, data analysis, A/B tests, quasi-experiments, and observational studies — in partnership with Operations, Marketing, and Product stakeholders • Evaluate, select, and implement the experimentation stack — prioritizing open-source and low-overhead tooling that can mature with the practice • Bring statistical rigor to ambiguous questions: help teams move from "we want to know if X works" to a study design that can actually answer that question • Communicate findings to non-technical audiences with clarity, including Senior Leadership and external partners
Job Requirements
- 5+ years working with data in analytical or engineering contexts, with demonstrable depth in SQL and Python
- Strong foundation in statistics and experimental design — you understand the difference between correlation and causation and know how to design for the latter
- Experience designing or executing controlled experiments or quasi-experimental studies (e.g., difference-in-differences, regression discontinuity, propensity score matching)
- Experience building and maintaining data models for downstream analytical use
- Fluency with git and collaborative development workflows
- Demonstrated ability to work cross-functionally and translate between technical and non-technical stakeholders
Benefits
- Comprehensive medical, dental, and vision options that prioritize accessibility and financial peace of mind
- Employer-funded HSA contributions
- Generous PTO, sick time, and 19 paid holidays with a winter break
- 12 weeks of fully paid parental leave after 26 consecutive weeks of service
- Monthly internet and mental health stipends
- Annual Wellness Stipend
- Home office and professional development stipends
- 403(b) retirement plan with employer contribution
- Sabbatical after 3 years of service
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