Data Engineer (Pricing & Monetization)

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.