Data Analytics Engineer

CSU Careers•Northridge, CA

1d•$6,492 - $7,759•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. It also creates and maintains logical and physical data models, analytics-ready datasets, semantic structures, and data transformations for operational and analytical use. The incumbent implements data validation, monitoring, logging, error handling, and reconciliation controls to improve accuracy, completeness, reliability, and recoverability. The role develops and maintains dashboards, reports, visualizations, certified datasets, and analytics solutions using approved platforms such as Tableau, Power BI, and QuickSight, and supports data services and pipelines for workflow automation, advanced analytics, and approved AI-enabled solutions. The position translates defined requirements into technical specifications, data mappings, process flows, test plans, and implementation tasks, and performs other duties as assigned. This is a regular, full-time position of 40 hours per week, Monday through Friday. It is currently hybrid, with three days in the office and two days remote, subject to change based on student or operational needs. The role may include evenings and weekends and has a one-year probationary period.

Requirements

Equivalent to a bachelor's degree in a related field and two years of relevant experience; additional experience demonstrating the required knowledge and abilities may substitute for the 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 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 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 be substituted for the required experience on a year-for-year basis.

Responsibilities

Develop, implement, maintain, and support data engineering, integration, reporting, and analytics solutions for academic and administrative operations.
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 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.