Data Engineer

<p>This role is responsible for cleaning, organizing, and maintaining customer and operational data so it can be reliably used for analytics, reporting, and basic modeling. The Data Engineer focuses on making messy data usable, consistent, and trustworthy for the rest of the business.</p> <p>This is a hands-on execution role, not a senior or architecture role. Success is measured by how clean, reliable, and easy-to-use the data becomes for analysts, product, and business teams.</p> <p> </p> <p><strong>What Success Looks Like</strong></p> <p>• Customer and operational datasets are clean, consistent, and easy to analyze<br>• Data pipelines run reliably with minimal errors<br>• Analysts and stakeholders spend less time fixing data and more time using it<br>• Basic data quality checks are in place and working</p> <p> </p> <p><strong>Key Responsibilities</strong></p> <p><strong>Data Cleanup & Maintenance</strong></p> <p>• Clean, normalize, and join customer, sales, and operational data from multiple sources<br>• Fix broken or unreliable data pipelines and tables<br>• Maintain consistent definitions for core customer and revenue metrics</p> <p> </p> <p><strong>Data Pipelines & Modeling</strong></p> <p>• Build and maintain simple, reliable data pipelines<br>• Create analytics-ready tables to support reporting, dashboards, and analysis<br>• Work with upstream teams to understand source data issues and limitations</p> <p> </p> <p><strong>Data Quality & Reliability</strong></p> <p>• Add basic data quality checks to catch missing, incorrect, or inconsistent data<br>• Monitor pipelines and resolve issues when data breaks<br>• Document datasets so others understand how to use them correctly</p> <p> </p> <p><strong>Collaboration & Support</strong></p> <p>• Support analysts and data scientists by providing clean, well-structured data<br>• Collaborate with product, sales, and operations teams to understand data needs<br>• Help answer ad-hoc data questions when needed</p> <p> </p> <p><strong>Key Performance Indicators (KPIs)</strong></p> <p>• Reliability and freshness of customer analytics datasets<br>• Reduction in data errors and rework required by analysts<br>• Time to resolve data pipeline issues<br>• Usability and clarity of analytics tables and documentation<br>• Stakeholder satisfaction with data quality and availability</p> <p> </p> <p><strong>What You’ll Bring</strong></p> <p>• 2–4 years of experience