Data & ML Engineer

Data & ML Engineer

Brand: Luxottica Wholesale North America Location: Mason, OH, US, 45040 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. Our Shared Services Team, accompany and enable others within the EssilorLuxottica collective to achieve their targets. They keep people and projects running smoothly, ensuring every part of our business is provided for and well taken care of. Join our global community of over 200,000 dedicated employees around the world in driving the transformation of the eyewear and eyecare industry. Discover more by following us on LinkedIn!

General Function: The Data & ML Engineer is a self-sufficient engineering professional responsible for designing, building, and operating scalable, secure, and reliable data and machine learning platforms primarily on Azure, with exposure to multi-cloud environments (AWS, GCP) where applicable. Expertise in programming languages such as Python or Scala, experience with data processing frameworks like Spark, and familiarity with container orchestration tools such as Kubernetes are essential for this role. Proficiency with CI/CD pipelines, DevOps, and MLOps practices is expected to ensure robust deployment and operationalization of analytics and AI solutions. This role complements the Applied Data Scientist by owning the engineering foundations required to operationalize analytics and AI solutions.

Major Duties And Responsibilities

• Managing the design, development, and operation of large-scale data ingestion, transformation, and storage pipelines
• Managing ML infrastructure, CI/CD, DevOps, and MLOps pipelines to support model training and deployment
• Managing platform performance, cost optimization, reliability, and availability
• Managing data security, governance, and regulatory compliance across platforms
• Managing collaboration with Applied Data Scientists to productionize models
• Organizing designing and organizing ETL/ELT workflows using Azure Data Factory and orchestration tools
• Structuring Lakehouse, Azure Data Lake, and Synapse environments for scalable analytics
• Structuring data formats, schemas, and versioning (Delta, Parquet, JSON, CSV)
• Structuring reusable data pipelines and ML components to accelerate delivery
• Organizing monitoring, logging, and alerting for data and ML pipelines
• Leading engineering best practices for scalable data and ML platforms
• Driving automation-first and infrastructure-as-code approaches
• Guiding solution design to ensure performance, resilience, and cost efficiency
• Leading troubleshooting and root-cause analysis for data and ML pipeline issues
• Mentoring engineers on cloud-native, big data, and MLOps practices

Basic Qualification

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

Preferred Qualifications

Master's degree in Computer Science, Engineering, or related discipline Experience with Azure Databricks, Spark, Synapse, and MLFlow Experience with Docker, AKS, APIs, and containerized ML workloads Experience with orchestration tools such as Azure Data Factory or Airflow Exposure to SAP CDC and enterprise data integration Experience working in agile, fast-paced, cross-functional environments

Technical & Behavioral Skills

Strong engineering ownership mindset with minimal supervision Ability to translate analytical requirements into scalable engineering solutions Strong collaboration skills with data scientists and business-facing teams Excellent problem-solving and troubleshooting capabilities Focus on reliability, scalability, and operational excellence