At Qu you’ll find a fun, dynamic, and diverse culture that celebrates team and individual success. Our people are down to earth, trail blazin’ folks who value innovation. 

While mostly virtual, we collaborate closely to produce leading-edge software solutions much needed in the restaurant industry.

At Qu you will:

• Define data architecture and infrastructure roadmaps, implementing best practices in data engineering to drive efficiency and value creation.
• Develop and maintain scalable data pipelines for structured (SQL), semi-structured (JSON, Parquet), and unstructured (logs, customer feedback) data.
• Define data strategies that align with the organization's overall objectives.
• Implement and manage ETL and reverse ETL pipelines, ensuring efficient data movement and transformation.
• Establish and enforce data quality, validation, and governance frameworks to maintain high levels of accuracy and reliability.
• Assemble large, complex datasets that meet both functional and non-functional business requirements.
• Analyze complex datasets to identify patterns, trends, and insights that drive business outcomes.
• Develop dashboards, reports, and data visualizations to communicate insights to stakeholders using tools like Looker, Tableau, or Power BI.
• Work closely with stakeholders, including the Product and Data Science teams, to support their data infrastructure needs while assisting with data-related technical issues.
• Own the entire data lifecycle—from ingestion to transformation to analytics—ensuring high performance, reliability, and usability.
• Lead data investigations, helping internal teams troubleshoot issues and improve data-driven decision-making.
• Proven experience leading data engineering teams, providing guidance, mentorship, and fostering a collaborative and productive work environment.
• Ensure compliance with relevant data protection laws and regulations.
• Roll up your sleeves—this role isnʼt about handing off work; itʼs about delivering solutions.

What we're looking for:

• 5+ years of experience in data engineering, data architecture, and analytics roles.
• Strong SQL skills—you should be able to write complex queries and optimize performance for large datasets.
• Experience with Python (or R) for data wrangling, automation, and analysis.
• Proficiency in data visualization tools (Looker, Tableau, Power BI) and ability to tell a story with data.
• Hands-on experience with cloud data platforms (Snowflake, BigQuery, Redshift, Databricks).
• Deep understanding of modern data architectures, including data lakes, event-driven processing, and scalable data warehousing.
• Experience designing data platforms that support AI-driven applications and machine learning workflows.
• Proven experience with ETL, reverse ETL, and data pipeline automation to enable seamless data flow across systems.
• Strong background in data quality, validation, and governance frameworks to ensure trust in data.
• Proven experience leading and mentoring data engineering teams while fostering a culture of collaboration.
• Familiarity with business intelligence and product analytics, including user behavior analysis and operational reporting.
• Excellent analytical and problem-solving skills with a strong ability to troubleshoot complex data challenges.
• A problem-solver who thrives in fast-paced, ambiguous environments.
• A doer—not just a planner. You execute, iterate, and deliver impact.