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HausHausSan Francisco, CA

Software Engineer

Build and maintain the Science Platform that runs geo-based experiments, statistical estimation, and daily analysis pipelines for causal marketing measurement. Requires strong Python, data pipeline, and orchestration experience plus collaboration with applied scientists.

165k – 205k
Hybrid3+ YOEData Engineering

About the role

What you’ll do

  • Build and evolve the data pipelines that fetch, aggregate, and transform KPI data from BigQuery across multiple geographies and granularities
  • Extend and maintain the statistical estimation library — implement new estimators, improve standard error methods, and optimize performance for large panel datasets
  • Improve the Metaflow-based analysis orchestration system that schedules and executes thousands of daily experiment analyses on Kubernetes
  • Design for reliability: build monitoring, alerting, and self-healing patterns for pipelines that run autonomously every day
  • Collaborate closely with applied scientists to translate research prototypes into production-grade code with proper testing, error handling, and observability
  • Work with product engineers to ensure analysis results are published correctly and flow cleanly into the customer-facing API and frontend
  • Use AI development tools as part of your daily workflow to accelerate delivery and explore solutions
  • Participate in on-call rotation and own the operational health of the science platform systems

Qualifications

  • 3+ years of experience building and shipping production software systems
  • Strong Python proficiency — clean, well-tested Python and comfort with the ecosystem (pandas, numpy, pytest, poetry)
  • Experience with data-intensive applications: large datasets, data pipelines, or ETL systems
  • Experience with SQL and analytical databases (BigQuery, Snowflake, or similar)
  • Comfort with cloud-native environments (GCP preferred)
  • Experience with workflow orchestration frameworks (Metaflow, Airflow, Dagster, Prefect, or similar) is a strong plus
  • Track record of working effectively with AI development tools (Claude, Cursor, Copilot, or similar)
  • Ability to collaborate productively with scientists and researchers — comfortable reading statistical code, understanding experimental design concepts
  • Excellent communication skills

Bonus Points

  • Earlier stage startup experience
  • Familiarity with statistical or scientific computing (scipy, scikit-learn, Bayesian methods)
  • Experience with Kubernetes and containerized workloads
  • Experience with event-driven architectures (Pub/Sub, message queues)
  • Experience with experimentation platforms, A/B testing infrastructure, or causal inference systems
  • Experience working across multiple interconnected repositories with coordinated release cycles

What We Offer

  • Flexible PTO
  • Equity
  • Top of the line health, dental, and vision insurance
  • WFH stipend
  • Events & Offsites
  • Free Lunch (in-office)
  • New Parent Leave

Skills

PythonpandasNumPyPytestPoetrySQLBigQueryGCPMetaflowKubernetesETLScipyscikit-learn

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