Data Engineer (Pricing & Monetization)

Alguna Logo
Alguna
United States (Remote)
80000 - 150000 USD / Year
  • Research
  • FullTime
  • Shift
  • Applications have closed

Who you are

  • You’re user-impact obsessed: You want to build customer-facing insights that help teams make better pricing and monetization decisions, not just internal dashboards.
  • You think in “insight → action”: You care about turning messy data into clear recommendations, experiments, and measurable outcomes.
  • You’re a 0→1 builder: You like blank-slate work: defining the data foundation, choosing tools, and setting patterns for how we build data products at Alguna.
  • You’re comfortable with ambiguity: Early-stage means fuzzy requirements and shifting priorities. You can still ship and iterate quickly.
  • You’re pragmatic and fast: You ship the simplest thing that delivers value, then refine once you learn what customers actually use.
  • You’re autonomous: You can make good decisions, unblock yourself, and own problems end-to-end.
  • You’re efficiency-obsessed: You automate repetitive work, reduce manual analysis, and shorten feedback loops.
  • You’re AI-enabled: You use AI tools to accelerate development, debugging, testing, documentation, and analysis—while owning correctness and security.
  • You’ve done this in production: You’ve built and operated a data stack before (0→1 or close to it).

What the job involves

  • 0→1: Build the data foundation for monetization products: Create the pipelines, models, and metric definitions needed to power pricing and monetization insights.

  • Customer-facing insights: Ship features customers trust, like:

  • Conversion and funnel performance

  • Cohorts, segmentation, and retention/expansion signals

  • Usage-to-revenue and feature adoption analysis

  • Experiment measurement (A/B tests) and learnings

  • Forecasting, anomaly detection, and “what changed?” explainability

  • Move fast with customers: Build → ship → learn → iterate. Stay close to real usage and feedback.

  • Data quality and trust: Implement testing, monitoring, and clear definitions so customers can rely on the outputs.

  • Improve internal developer experience: Make data work easy for the team: automation, reusable patterns, docs, and observability.

  • Write it down: Short proposals and decision docs to align quickly and keep context.

  • Be pragmatic: We’re still finding product-market fit. Not everything will be polished at first; we’ll prioritize learning and customer value.

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