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Alguna · 10 hours ago

Data Engineer (Pricing & Monetization)

United States

Full-time

Remote

Mid Level

$80K/yr - $150K/yr

Alguna is focused on building customer-facing insights that enhance pricing and monetization decisions. The Data Engineer will create data pipelines and models to facilitate this process while ensuring data quality and improving the developer experience.

B2BBillingSaaSSoftware

Responsibilities

Build the data foundation for monetization products: Create the pipelines, models, and metric definitions needed to power pricing and monetization 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

Build → ship → learn → iterate. Stay close to real usage and feedback

Implement testing, monitoring, and clear definitions so customers can rely on the outputs

Make data work easy for the team: automation, reusable patterns, docs, and observability

Short proposals and decision docs to align quickly and keep context

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

Qualification

Data pipeline developmentData modelingExperiment measurementAI tools usageData quality assuranceCustomer-facing insightsAutomationPragmatismDocumentation

Required

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)

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