Principal, Data Engineering

Alcon•Fort Worth, TX

16h

About The Position

The Principal Data Engineer is a senior technical leader responsible for designing, building, and evolving scalable, reliable, and secure enterprise data platforms and data products. The role provides hands-on technical leadership across data architecture, data pipelines, cloud platforms, data quality, governance, and engineering practices. It partners with Data Science, BI/Analytics, product teams, business stakeholders, and technology teams to make trusted, well-engineered data available for analytics, AI, machine learning, reporting, and operational use cases. This is a technical leadership position rather than a people-management role, with significant responsibility for architecture decisions, engineering standards, mentoring, and resolving complex data engineering challenges. The Principal Data Engineer also enables AI and analytics through reliable data foundations, curated datasets, appropriate data architecture, lineage, quality controls, monitoring, and reusable data services.

Requirements

Bachelor’s degree or equivalent years of directly related experience; alternatively, high school plus 13 years, an associate degree plus 9 years, an M.S. plus 2 years, or a Ph.D. plus 0 years.
Ability to fluently read, write, understand, and communicate in English.
Five years of relevant experience.

Nice To Haves

Power BI.
Business intelligence.

Responsibilities

Serve as a technical leader for enterprise data engineering solutions.
Design scalable data architectures, data pipelines, integration patterns, and reusable data services.
Establish engineering standards for data ingestion, transformation, storage, orchestration, testing, monitoring, and deployment.
Lead technical design reviews and guide teams toward scalable solutions rather than short-term fixes.
Evaluate emerging data technologies and recommend adoption where they provide measurable value.
Design and develop robust batch and streaming data pipelines across enterprise data sources.
Build reusable frameworks that improve development speed, consistency, observability, and maintainability.
Optimize data pipelines for performance, scalability, reliability, and cost.
Establish monitoring and alerting to proactively identify pipeline and data-quality issues.
Improve pipeline reliability, freshness, and data-quality outcomes.
Partner with Data Scientists and analytics teams to create reliable, AI-ready data foundations.
Develop data pipelines and curated datasets supporting machine learning, forecasting, optimization, Generative AI, and advanced analytics.
Support production AI solutions through appropriate data architecture, lineage, quality controls, monitoring, and reusable data services.
Work closely with ML/AI engineering teams to enable scalable model development and deployment.
Mentor and guide teams in data engineering practices.