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

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

29 May, 26

Salary

13000.0

Posted On

28 Feb, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Database Engines, Data Principles, Data Governance, Data Quality, SQL, NoSQL, Python, Spark, MapReduce, R, Tableau, Statistical Analysis, Machine Learning, Class Lectures, Grading

Industry

Higher Education

Description

Company Description 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. Job Description Columbia University’s Master's in Applied Analytics program seeks experienced industry professionals to serve as a part-time Lecturer 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. 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. Qualifications 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. 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. Preferred Skills & Experience Knowledge of MapReduce strongly desired. Other software or programming languages like R and Tableau. Statistical and Machine learning knowledge. University teaching experience. Additional Information Salary range: $11,000 - $13,000 per semester long course Please submit a resume inclusive of university teaching experience. All your information will be kept confidential according to EEO guidelines. Columbia University is an Equal Opportunity Employer / Disability / Veteran Employee Job Category: Faculty Job Term: 2026 FALL Role: Lecturer, Part-time Department: Applied Analytics Division: Masters Program: Applied Analytics Academic Program: APAN

Responsibilities

The Lecturer will lead class lectures, instructional activities, and classroom discussions for a graduate-level course on the fundamentals of data engineering. Responsibilities also include monitoring student concerns, evaluating work, grading assessments, and conducting office hours.