Data & ML Engineer

EssilorLuxottica Group•Dallas, TX

1d•Onsite

About The Position

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. EssilorLuxottica is 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. Our Shared Services Team accompanies and enables others within the EssilorLuxottica collective to achieve their targets. Join our global community of over 200,000 dedicated employees around the world in driving the transformation of the eyewear and eyecare industry.

Requirements

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

Nice To Haves

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

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