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# Data Analyst

- **Company:** Goodhabitz
- **Location:** EINDHOVEN, Netherlands
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Data Cleansing, Data Hub, Python (Programming Language), Machine Learning, Standard Sql, SQL Databases, Tableau (Software), Large Language Models, Data Lakes, Databricks
- **Published:** September 22, 2026
- **Apply:** https://startup.jobs/senior-data-analyst-goodhabitzhq-10157095

## About the Role

You bring a strong analytical and statistical foundation, sound judgement and the curiosity to keep asking better questions. You also know that great analysis is a team effort: it takes collaboration, empathy for the people using the work and the confidence to make complexity clear.

 * Analytical experience: 7+ years as a (Senior) Data Analyst, preferably in a B2B organisation with subscription or recurring-revenue dynamics.
 * Statistical reasoning: You are comfortable applying statistical reasoning to time-based and cohort data. In this role, that ability is core to the work.

 * Cross-functional impact: A strong track record of leading complex analysis across teams and raising the analytical bar for the people around you.
 * SQL & Python: Expert-level SQL skills for querying and transforming large datasets, advanced Python skills for data preparation and statistical validation.

 * Self-service BI & Insights: Proven experience in offering self-service to non-technical stakeholders via dashboards or AI/LLMs.

 * Predictive & Machine Learning: Experience designing, validating, rolling out and monitoring predictive models (e.g. churn or conversion prediction), including basic safeguards like train/test separation and drift monitoring.

Preferred qualifications (nice-to-have)

 * Databricks: Confidence working in Databricks and Delta Lake.
 * Experience with Tableau, Tableau Pulse

## Description

* Provide statistical sound insights - cohort maturity, sample size, aggregation bias, construct validity - before analysis reach senior stakeholders.

 * Shape the team's data model and analytics conventions, and mentor other analysts on data quality.

 * Translate complex analysis into clear business language so senior stakeholders can decide with confidence.

 * Own end-to-end analysis across revenue, product, finance and marketing domain.

 * Align Revenue, Finance, Product and Operations on shared metric definitions and resolve conflicts into one documented decision.
 * Build self-service Tableau dashboards or access via LLMs for non-technical stakeholders.

 * Write clean, documented SQL/code to support the centralised data hub.

 * Partner with Data Engineers on data model design and champion data quality.


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