Data Analytics Engineer

California State University, Northridge•Los Angeles, CA

2d•$6,492 - $9,458•Hybrid

About The Position

Under the operational direction of the Data & Analytics Lead and general guidance of the Technology Strategy Officer (TSO), the Data Analytics Engineer develops, implements, maintains, and supports data engineering, integration, reporting, and analytics solutions for academic and administrative operations. The role designs, develops, tests, deploys, and maintains ETL/ELT data pipelines, APIs, integrations, scripts, and scheduled data processes, and creates and maintains logical and physical data models, analytics-ready datasets, semantic structures, and data transformations for operational and analytical use. The engineer implements data validation, monitoring, logging, error handling, and reconciliation controls to improve data accuracy, completeness, reliability, and recoverability. The role also develops and maintains dashboards, reports, visualizations, certified datasets, and related analytics solutions using approved platforms such as Tableau, Power BI, and QuickSight. The position supports data services and pipelines used by workflow automation, advanced analytics, and approved AI-enabled solutions, and translates defined requirements into technical specifications, mappings, process flows, test plans, and implementation tasks. Other duties may be assigned. This is a regular, full-time position, scheduled for 40 hours per week, Monday through Friday. The position is currently hybrid, with three days in the office and two days remote, subject to change based on student or operational needs. The schedule may include evenings and weekends. The position has a one-year probationary period. The anticipated hiring range is $6,492–$7,759 per month; the classification salary range is $6,492–$9,458 per month, depending on qualifications and experience.

Requirements

Equivalent to a bachelor's degree in a related field and two years of relevant experience; additional relevant experience demonstrating the required knowledge and abilities may substitute for required education on a year-for-year basis.
Comprehensive knowledge of data engineering principles and practices, including ETL/ELT pipelines, APIs, data modeling, data transformations, cloud and SaaS data environments, data warehouses, lakes, and lakehouse platforms.
Demonstrated skill in developing scalable, reliable, and maintainable data solutions.
Strong analytical and technical problem-solving skills, including troubleshooting data pipelines, integrations, performance issues, and data anomalies.
Ability to implement data quality and governance controls and collaborate with technical and functional partners to coordinate dependencies and deliver data-driven solutions across systems and platforms.
Thorough knowledge of reporting and analytics principles, with demonstrated skill in developing dashboards, reports, visualizations, certified datasets, reporting structures, and semantic layers using platforms such as Tableau, Power BI, and QuickSight.
Ability to validate data, metrics, calculations, and outputs and resolve reporting accuracy, usability, and performance issues.
Ability to gather and translate data, reporting, integration, and automation requirements into technical specifications, mappings, process flows, test plans, and implementation tasks.
Ability to produce clear technical documentation while applying established accessibility, security, privacy, architecture, and data governance standards.

Nice To Haves

A degree in Computer Science, Data Analytics, Data Science, Information Systems, or a related field is preferred.
Experience with statistical analysis, predictive modeling, machine learning, or data science methods is desirable.
An advanced degree in a related field may substitute for required experience on a year-for-year basis.

Responsibilities

Design, develop, test, deploy, and maintain ETL/ELT data pipelines, APIs, integrations, scripts, and scheduled data processes.
Create and maintain logical and physical data models, analytics-ready datasets, semantic structures, and data transformations for operational and analytical use.
Implement data validation, monitoring, logging, error handling, and reconciliation controls to improve accuracy, completeness, reliability, and recoverability.
Develop, enhance, and maintain dashboards, reports, visualizations, certified datasets, and related analytics solutions using approved platforms such as Tableau, Power BI, and QuickSight.
Develop and support data services and pipelines used by workflow automation, advanced analytics, and approved AI-enabled solutions.
Translate defined requirements into technical specifications, data mappings, process flows, test plans, and implementation tasks.
Perform other duties as assigned.