Data Engineer at Locus Robotics

Locus Robotics is a global leader in warehouse automation, delivering unmatched flexibility and unlimited throughput, and actionable intelligence to optimize operations. Powered by LocusONE, an AI-driven platform, our advanced autonomous mobile robots seamlessly integrate into existing warehouse environments to enhance efficiency, reduce costs, and scale operations with ease.

Trusted by over 150 industry leading retail, healthcare, 3PL, and industrial brands in over 350 sites worldwide, Locus enables warehouse operators to achieve rapid ROI, minimize labor costs, and continuously improve productivity. Our industry-first Robots-as-a-Service (RaaS) model ensures ongoing innovation, scalability, and cost-effectiveness without the burden of significant capital investments. With proven capabilities in diverse workflows—from picking and replenishment to sorting and pack-out—Locus Robotics empowers businesses to meet peak demands and adapt to ever-changing operational needs.

Are you a skilled Python Data Engineer with a passion for building scalable, production-level systems and an interest in machine learning? We want to hear from you! At Locus Robotics, we’re evolving our data infrastructure to support advanced machine learning capabilities and real-time analytics. As a key member of our team, you’ll help migrate our existing codebase into machine learning territory, drive innovation across multiple deployment sites, and collaborate closely with our optimization and engineering teams. You'll develop robust Python code, analyze system performance, and contribute to deployment pipelines that power critical operations. If you’re an independent, hands-on engineer who thrives on writing production-grade Python code, analyzing logs, debugging complex systems, and delivering real-world solutions, this role is for you.

This is a remote position based in England, Scotland, Portugal, Poland, or Spain. Candidates must be authorized to work in one of these countries without the need for work sponsorship.

Responsibilities:

• Develop and maintain Python-based systems deployed across remote platforms.
• Contribute to and improve data pipelines, ensuring reliable and efficient system updates. 
• Build and enhance features for real-time data analysis and system monitoring to ensure high uptime and efficiency.
• Collaborate with data scientists and engineers to support advanced analytics and machine learning workflows.
• Support the migration of our codebase toward machine learning capabilities by building scalable, maintainable solutions.
• Analyze system logs and performance to debug issues and optimize operations using forensic analysis tools.

Qualifications:

• Bachelor's or Master’s degree in Computer Science, Mathematics, Data Analytics, or a related field.
• 3+ years of experience developing and deploying production-grade Python software.
• 3+ years of experience with Python and high-performance data libraries such as Polars and Pandas.
• Proficiency with JavaScript, SQL, and KQL.
• Experience with Extract, Transform, Load (ETL), Data Streaming, and Reconciliation.
• Experience building and maintaining deployment pipelines, including DevOps tools like Ansible, and containerization with Docker.
• Proficiency with cloud platforms (AWS or Azure) for deploying and scaling data systems.
  
• Highly desired experience with Azure, particularly Lakehouse and Eventhouse architectures.
• Experience with relevant infrastructure and tools including NATS, Power BI, Apache Spark/Databricks, and PySpark.
• Hands-on experience with data warehousing methodologies and optimization libraries (e.g., OR-Tools).
• Experience with log analysis, forensic debugging, and system performance tuning.
• Exposure to cloud-based systems.
• Familiarity with Agile/SCRUM methodologies in collaborative development workflows.
• Proficient English communication skills, both written and verbal, with the ability to engage diverse audiences effectively.
• Excellent analytical and problem-solving skills, with the ability to contribute effectively in a collaborative team environment.