> Markdown version of [/jobs/ext/1781251-data-ml-engineer](https://www.wearedevelopers.com/jobs/ext/1781251-data-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data & ML Engineer

- **Company:** EssilorLuxottica
- **Location:** Mason, OH, United States
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Big Data, Bioinformatics, Cloud Engineering, Continuous Integration, Extract Transform Load (ETL), Data Security, DevOps, JSON, Python (Programming Language), Machine Learning, Azure Data Lake, SAP (Applications), Scala (Programming Language), SQL Databases, Management of Software Versions, Workflow Management Systems, Parquet, Data Logging, Scripting, Azure Data Factory, Apache Spark, Multi-Cloud, Kubernetes, Information Technology, Integration Frameworks, Azure AKS, Machine Learning Operations, Azure Synapse Analytics, Data Pipelines, Docker, Databricks, Programming Languages
- **Published:** July 2, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=231ded9c25f76bbd

## About the Role

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
This position is not available for visa sponsorship or transfer. Candidates must have authorization to work in the United States
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, We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, gender, national origin, social origin, social condition, being perceived as a victim of domestic violence, sexual aggression or stalking, religion, age, disability, sexual orientation, gender identity or expression, citizenship, ancestry, veteran or military status, marital status, pregnancy (including unlawful discrimination on the basis of a legally protected pregnancy or maternity leave), genetic information or any other characteristics protected by law. Native Americans in the US receive preference in accordance with Tribal Law.

## Description

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
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
Organizing 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
Leading engineering best practices for scalable data and ML platformsDriving 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


## Related Videos

- [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json)
- [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform)
- [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered)
- [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture)
- [Introducing JSON Structure](https://www.wearedevelopers.com/videos/100219-introducing-json-structure)
- [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market)
- [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [7 Most Popular Web Developer Jobs in Europe](https://www.wearedevelopers.com/magazine/163-7-most-popular-web-developer-jobs-in-europe)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)