Data & ML Engineer

Data & ML Engineer

Brand: Luxottica Wholesale North America Location: Dallas, TX, US, 75204 Position: Full-Time Total Rewards: Benefits/Incentive Information If you've worn a pair of glasses, we've already met. We are a global leader in the design, manufacture, and distribution of ophthalmic lenses, frames, and sunglasses. We offer our industry stakeholders in over 150 countries access to a global platform of high-quality vision care products such as the Essilor brand, with Varilux, Crizal, Eyezen, Stellest and Transitions, iconic brands that consumers love such as Ray-Ban, Oakley, Persol and Oliver Peoples, as well as a network that offers consumers high-quality vision care and best-in-class shopping experiences such as Sunglass Hut, LensCrafters, and Target Optical, and leading e-commerce platforms. Our portfolio of more than 150 renowned brands span various categories, from frames, lenses and instruments to brick and mortar and digital distribution as well as mid-range to premium segments. Join our global community of over 200,000 dedicated employees around the world in driving the transformation of the eyewear and eyecare industry.

This position is not available for visa sponsorship or transfer. Candidates must have authorization to work in the United States.

General Function

The Data & ML Engineer designs, builds, and operates scalable, secure, and reliable data and machine learning platforms primarily on Azure, with exposure to AWS and GCP when applicable. This role requires expertise in Python or Scala, data processing frameworks (e.g., Spark), and container orchestration tools like Kubernetes. Strong proficiency in CI/CD, DevOps, and MLOps is essential to support the deployment and operationalization of analytics and AI solutions. The role partners closely with Applied Data Scientists to enable production-ready models and robust engineering foundations.

Major Duties And Responsibilities

• Design, develop, and operate large-scale data ingestion, transformation, and storage pipelines
• Manage ML infrastructure and CI/CD, DevOps, and MLOps pipelines for model training and deployment
• Optimize platform performance, reliability, cost, and availability
• Ensure data security, governance, and regulatory compliance
• Collaborate with Applied Data Scientists to productionize models
• Design ETL/ELT workflows using Azure Data Factory and orchestration tools
• Structure Lakehouse, Data Lake, and Synapse environments for scalable analytics
• Organize data formats, schemas, and versioning (Delta, Parquet, JSON, CSV)
• Build reusable pipelines and ML components to accelerate delivery
• Implement monitoring, logging, and alerting for data and ML pipelines
• Champion best practices for scalable data and ML platforms
• Drive automation and infrastructure-as-code approaches
• Guide solution design for performance, resilience, and cost efficiency
• Lead troubleshooting and root-cause analysis for pipeline issues
• Mentor engineers in cloud-native, big data, and MLOps practices

Basic Qualifications

Bachelor's degree in Computer Science, Engineering, or related field Experience as a Data Engineer, ML Engineer, or Platform Engineer Strong hands on experience with Azure and big data platforms Proficiency in Python, SQL, Scala, and scripting languages Experience building production grade data pipelines Ability to independently deliver complex data and ML engineering solutions

Preferred Qualifications

Master's degree in related discipline Experience with Azure Databricks, Spark, Synapse, MLFlow Experience with Docker, AKS, APIs, and containerized ML workloads Experience with Azure Data Factory or Airflow Exposure to SAP CDC and enterprise data integration Experience in agile, fast paced, cross functional environments Strong ownership and independence Ability to translate analytical needs into scalable engineering solutions Strong collaboration skills with data scientists and business teams Excellent problem solving and troubleshooting capabilities Focus on reliability, scalability, and operational excellence