Data Engineer

San José State University Research Foundation • San Jose, CA

1d

About The Position

The Data Engineer supports San José State University’s Campus Data Warehousing and Business Intelligence initiatives by developing, integrating, and maintaining enterprise data solutions. The role contributes to data pipelines and data models that bring information from multiple university systems into trusted, governed data platforms for institutional reporting, analytics, and data-driven decision-making.

Working under the guidance of senior technical staff, the Data Engineer applies computer science, information systems, database, programming, data architecture, and cloud-platform knowledge to develop and support enterprise data solutions. The position works with technical teams and university stakeholders to analyze requirements, troubleshoot data and system issues, design technical solutions, document requirements, and support vendor and locally developed systems. It reports to the Project Director and regularly interacts with SJSU Information Technology. There are no supervisory responsibilities, though the position may provide technical direction, advice, or mentoring.

The role requires a bachelor’s degree or equivalent in a relevant technical or quantitative field and at least one year of related professional experience. Compensation is $77,000–$85,000, depending on experience and qualifications. The SJSURF describes its benefits package as available to benefited employees.

Requirements

• Bachelor’s degree or equivalent in Computer Science, Computer Engineering, Information Systems, Data Science, Software Engineering, or a closely related technical or quantitative field that includes relevant computing coursework.
• At least one year of professional experience in data engineering, database development, software development, data integration, data analytics development, business applications, or a closely related technical field.
• Experience applying programming, database, data integration, and/or data analytics principles to information systems through professional employment or internships.
• Experience using SQL and programming or scripting languages such as Python or Java.
• Experience with relational databases or data platforms such as Oracle, Snowflake, or Databricks.
• Experience with cloud platforms such as AWS or Google Cloud Platform.
• Professional knowledge of programming, database systems, data structures, data integration, and data engineering concepts.
• Working knowledge of relational database concepts and SQL, including developing and troubleshooting queries.
• Knowledge of programming or scripting such as Python, Java, JavaScript, bash, cron syntax, or similar technologies.
• Working knowledge of data pipeline development, data transformation, ETL/ELT, metadata, and data warehousing concepts.
• Knowledge of cloud computing and modern data platforms such as AWS, Google Cloud Platform, Databricks, Snowflake, Apache Airflow, or comparable technologies.
• Ability to analyze SQL, source data, application logic, system logs, and code to identify and troubleshoot technical issues.
• Ability to work with data from multiple source systems and apply technical requirements and business rules to data solutions.
• Knowledge of software and systems development lifecycle methodologies, including Agile and Waterfall.
• Working knowledge of application development, testing, deployment, monitoring, and production-support practices.
• Knowledge of analytical concepts including statistical analysis, data mining, forecasting, regression, and predictive modeling.
• Ability to analyze structured and unstructured datasets using appropriate technical and analytical methods.
• Ability to prepare and maintain technical documentation, including data mappings, requirements, integration logic, and operational procedures.
• Ability to communicate technical information and collaborate effectively with technical staff and university stakeholders.
• Ability to analyze technical problems, identify potential solutions, and escalate or collaborate on complex issues as appropriate.
• Ability to learn and apply emerging technologies related to data engineering, analytics, databases, and cloud computing.
• Strong analytical curiosity and comfort working with structured and unstructured datasets.
• Ability to complete a satisfactory background check, including a criminal records check, before an offer can be made.
• Ability to use a computer keyboard and read a video display terminal regularly; lift, relocate, and integrate IT and lab-related equipment; listen to and appropriately respond to verbal requests; and discuss and respond to inquiries from employees, university faculty and staff, and vendors.

Nice To Haves

• Master’s degree in Computer Science (Software Engineering), Business Administration, or a closely related technical or quantitative field.
• Experience developing and maintaining ETL/ELT processes and micro-batch data pipelines.
• Experience with visualization and analytics platforms such as Tableau, Looker, AWS QuickSight, or comparable technologies.
• Exposure to agentic AI, speech-to-text, and Retrieval-Augmented Generation (RAG) technologies.
• Some experience or coursework in data mining, forecasting, deep learning, machine learning, or predictive modeling.

Responsibilities

• Develop, test, implement, and maintain data pipelines and integration processes that move data from enterprise source systems into the data warehouse and analytics platforms.
• Develop and maintain SQL queries, database objects, data transformations, and data models for reporting and analytics.
• Use programming and scripting languages to develop, maintain, and support data solutions.
• Analyze source-system structures, relationships, and business rules for integration into enterprise data platforms.
• Troubleshoot pipeline, integration, database, and production data issues, collaborating with senior developers and technical teams on complex issues.
• Develop and maintain processes for validating data quality, integrity, consistency, and completeness.
• Develop and deploy scalable, secure, and reliable data solutions in cloud environments.
• Support solutions using relational databases, cloud environments, data lakehouse technologies, and platforms such as Oracle, Snowflake, Databricks, AWS, and Google Cloud Platform.
• Apply data architecture, ETL/ELT, data warehousing, metadata management, and database design principles.
• Collaborate with technical teams and functional stakeholders to translate business needs into technical data requirements and solutions.
• Support analytical methodologies such as segmentation, statistical analysis, regression, forecasting, data mining, and predictive modeling as appropriate.
• Provide production support, including monitoring, troubleshooting, incident resolution, testing, deployment, and maintenance, while ensuring SLA compliance.
• Analyze structured and unstructured datasets to identify useful trends, anomalies, relationships, and information for university decision-making.
• Build trusted relationships with internal and external stakeholders through reliable and accurate data work.
• Follow IT policies and standards for consistency across the SJSU campus.
• Stay current with technology trends and share learnings with the team.

Benefits

• Four company health insurance plans to choose from, with employee contributions varying by plan and coverage level.
• Employer-paid dental and vision insurance for employees and eligible dependents.
• Life, AD&D, and LTD coverage, with supplemental coverage opportunities.
• 14 paid federal and state holidays.
• Retirement plan with employee and employer 403(b) contribution components; employer contributions vest immediately.
• Vacation-hour accruals and separate sick-hour accumulations.
• Employee discounts.
• Paid training and professional-development conferences.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level