Lead Data Engineer: Cloud Data Platforms

Hace 16 horas

Reynosa, Tamaulipas, México Corning Incorporated Jornada completa

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Are you ready to define the technical direction of a growing Data Engineering organization while remaining hands-on with modern cloud, manufacturing, and AI-enabled data solutions?

Join Corning’s Optical Communications team and help shape scalable data platforms that support manufacturing, operations, analytics, reporting, and AI/ML initiatives across a global organization.

What is your role?

As a Lead Data Engineer, you will serve as a senior individual contributor responsible for defining technical direction, leading complex Data Engineering initiatives, and building scalable, reliable, and cost-effective data solutions.

You will remain hands-on with data pipelines, Python, SQL, cloud technologies, and distributed data processing while also influencing architecture, engineering standards, data quality, governance, and platform strategy.

This position does not currently have direct reports. Leadership will be demonstrated through technical expertise, project ownership, mentoring, and cross-functional influence.

Major Responsibilities And Tasks Of The Position

Define and evolve scalable data architectures, platforms, pipelines, and curated data products that support manufacturing, analytics, reporting, and AI/ML applications.
Lead the design, development, optimization, and delivery of complex ETL/ELT pipelines and distributed data-processing solutions.
Establish and promote Data Engineering standards, development patterns, data-quality frameworks, observability practices, documentation, and platform governance.
Provide technical leadership across projects and teams through architecture reviews, mentoring, troubleshooting, code reviews, and cross-functional collaboration.
Partner with manufacturing, analytics, Decision Intelligence, IT, architecture, application, and business teams to align data solutions with strategic priorities.

What do you need to have?

Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, or another related technical discipline.
At least 5 years of experience in Data Engineering, including designing, building, optimizing, and maintaining production data pipelines, data warehouses, data lakes, or lakehouse environments.
At least 5 years of professional programming experience using Python, with the ability to write clean, testable, maintainable, and scalable code.
Strong hands-on experience building data pipelines and working with Apache Spark, PySpark, SparkSQL, or comparable distributed data-processing technologies.
Experience working with manufacturing, production, industrial, operational, equipment, quality, supply-chain, sensor, or time-series data.
Demonstrated technical leadership, including leading projects, defining architecture or engineering standards, mentoring engineers, and influencing technical decisions.
Advanced SQL, data modeling, and database experience across relational databases, data warehouses, data lakes, or modern cloud data platforms.
Cloud-platform experience using AWS, Azure, or Google Cloud. AWS experience is strongly preferred.
Strong communication skills, with the ability to explain technical strategies and architecture decisions to technical teams, business stakeholders, and senior leaders.

What would be helpful?

Experience with Databricks, Snowflake, Delta Lake, Parquet, Iceberg, PostgreSQL, MySQL, Oracle, or Microsoft SQL Server.
Experience with Kafka, Flink, or other streaming and near-real-time data technologies.
Experience with Airflow, Dagster, Prefect, or another workflow-orchestration platform.
Exposure to AI/ML data pipelines, Large Language Models, AI agents, intelligent data observability, or AI-enabled data-quality solutions.
Familiarity with LangChain, LlamaIndex, Semantic Kernel, or prompt-engineering practices.
Experience with industrial IoT, operational technology systems, PI Integrator, Camstar, or Maximo.
Experience with infrastructure-as-code technologies such as Terraform or CloudFormation.
Experience with Informatica, MuleSoft, SSIS, or other enterprise data-integration tools.

What do we offer?

A hybrid role based in Monterrey or Reynosa, Mexico.
The opportunity to define technical direction while remaining hands-on with Data Engineering delivery.
Direct influence over data architecture, platform strategy, engineering standards, and data-quality practices.
Exposure to modern cloud platforms, distributed processing, manufacturing data, AI/ML, and intelligent automation.
The opportunity to mentor Data Engineers and Senior Data Engineers and help build the next generation of techni

Lead Data Engineer: Cloud Data Platforms

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