Senior Data Engineer - Analyst

March 15

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Qu POS

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Qu POS is a technology company that provides a unified commerce platform for quick service and fast casual restaurant chains. Their platform integrates point of sale (POS), online ordering, drive-thru kiosks, and third-party marketplace connections to create seamless on- and off-premise ordering experiences. This enables restaurant operators to manage multi-channel and menu ecosystems efficiently, enhancing brand loyalty and operational accuracy. By leveraging data-rich insights, Qu POS aims to revolutionize restaurant commerce by moving operations into the digital age, focusing on scalability and growth.

Restaurant & Hospitality Point of Sale • Restaurant Analytics • Restaurant Software • Fast Casual • Quick Service Restaurants

51 - 200 employees

☁️ SaaS

🏢 Enterprise

🛒 Retail

📋 Description

• 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.

🎯 Requirements

• 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.