Adjunct Lecturer, Fundamentals of Data Engineering (On-Campus, Fall '26)

Columbia University has been a leader in higher education in the nation and around the world for more than 250 years. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds in pursuit of greater human understanding, pioneering new discoveries, and service to society.

The School of Professional Studies at Columbia University offers innovative and rigorous programs that integrate knowledge across disciplinary boundaries, combine theory with practice, leverage the expertise of our students and faculty, and connect global constituencies. Through twenty professional master's degrees, courses for advancement and graduate school preparation, certificate programs, summer courses, high school programs, and a program for learning English as a second language, the School of Professional Studies transforms knowledge and understanding in service of the greater good.

Columbia University’s Master's i n Applied Analytics program seeks experienced industry professionals to serve as a part-time L ecturer for a graduate-level course in Managing Data.

The Fundamentals of Data Engineering course provides students with a foundational context for managing data so that it can be leveraged and used with confidence. Analytic teams work closely with technology partners in managing data. Languages and techniques unique to each team can impede cooperation. To bridge this gap, this course provides a broad overview of data technology concepts including database engines and associated technologies and exposes students to foundational data principles, governance processes, and organizational prerequisites needed to overcome challenges to ensure data quality.

Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty possessing outstanding academic training as well as a record of accomplishment as practitioners in an applied industry setting.

Responsibilities

Lead class lectures, instructional activities, and classroom discussion. Attend all class sessions.; Monitor and address student concerns and inquiries.; Evaluate, grade student work and assessments.; Conduct office hours.

Requirements

Doctoral degree or equivalent required, in an area related to data science, statistics, computer science, or another discipline that provided rigorous training in quantitative analytics.; Knowledge of databases, topics in Big Data, and Data Analysis.; Knowledge of SQL and NoSQL databases.; Knowledge of Python and Spark.; 10+ years of related applied professional experience.

Nice to have

Knowledge of MapReduce strongly desired.; Other software or programming languages like R and Tableau.; Statistical and Machine learning knowledge.; University teaching experience.