Data Engineer; Pricing & Monetization

Position

Data Engineer (Pricing & Monetization)

Location

Air Ronge

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.

Reference

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