Data Engineer (Intermediate OR Senior)

Teacher Retirement System of Texas (TRS)•Austin, TX

3d•$98,214 - $149,898•Onsite

About The Position

This vacancy will be filled at one of two levels: Data Engineer or Data Engineer Senior. The selected applicant will be offered the level that most closely matches their education and experience. The role supports the Investment Data Modernization Division by designing, developing, and optimizing data pipelines, data models, platforms, and integrations. It includes maintaining data warehouses, conducting data analysis, generating reports, and contributing to Agile team activities. The incumbent collaborates with the Data Engineering Team, IT staff, enterprise stakeholders, and agency employees. At the senior level, the role also architects and enhances data platforms and pipelines, oversees advanced data warehouse maintenance, leads strategic data initiatives and larger analysis projects, and guides the evolution of Investment data platforms and systems. The incumbent serves as a technical advisor to Investment teams, implements modern platform capabilities, delivers high-performing solutions, and supports their adoption across the business. Both levels support the integration, security, and delivery of investment data for agency operations.

Requirements

Bachelor's degree from an accredited college or university in computer information systems, computer science, data management, information systems, information science, mathematics, or a closely related field; alternatively, a high school diploma or equivalent and additional full-time experience in data pipeline builds and implementation may substitute on an equivalent year-for-year basis.
Three to five years of full-time, directly related, progressively responsible experience in data pipeline builds and implementation; using ETL tools to create ETL scripts; and Power BI or an equivalent modern data visualization package or related experience. Experience may be concurrent.
A master's or doctoral degree in a closely related field may substitute for experience on an equivalent year-for-year basis.
Strong knowledge of business intelligence tools, databases, SQL, and object-oriented programming languages to support data analysis and predictive analytics.
Knowledge of database modeling, data warehousing principles, and system development life cycle concepts.
Knowledge of data platforms, ETL concepts, data architecture and modeling, Microsoft Fabric, Databricks, Snowflake, Spark, and cloud platforms; advanced knowledge is applicable at the senior level.
Knowledge of data encryption (REST, SOAP, XML, JSON) and web services (SSL, SSH, SFTP, Certificates); advanced knowledge is applicable at the senior level.
Proficiency in Python and SQL; advanced proficiency is applicable at the senior level.
Technical and statistical analysis skills to deliver clear, concise, visually appealing management metrics and reports.
Ability to write report queries and understand relational databases.
Business process analysis and problem-solving skills.
Ability to complete detailed work accurately and plan, organize, and prioritize assignments in a high-volume, fast-paced, changing environment.
Ability to use a computer in a Windows environment, including Microsoft Office word processing, spreadsheet, and other business software.
Effective written and verbal communication skills, including explaining complex information clearly and preparing precise policies, procedures, training, or other materials.
Ability to work in a team and influence a diverse group of stakeholders.
Ability to adapt to competing and shifting priorities, continually learn tools and concepts, and pursue ongoing professional development.
Ability to establish and maintain effective working relationships with co-workers, agency staff, and external contacts.
Ability to participate in Scrum activities and understand Agile practices; advanced understanding is applicable at the senior level.
No registration, certification, or licensure is required.

Nice To Haves

For Data Engineer: Experience with data architecture and modeling.
For Data Engineer: Experience with Microsoft Fabric, Databricks, Snowflake, Spark, SQL, Python, and cloud platforms.
For Data Engineer: Experience with data encryption (REST, SOAP, XML, JSON) and web services (SSL, SSH, SFTP, Certificates).
For Data Engineer Senior: Advanced experience with data architecture and modeling.
For Data Engineer Senior: Advanced experience with Microsoft Fabric, Databricks, Snowflake, Spark, SQL, Python, and cloud platforms.
For Data Engineer Senior: Advanced experience with data encryption (REST, SOAP, XML, JSON) and web services (SSL, SSH, SFTP, Certificates).

Responsibilities

Develop and manage data platforms.
Design, implement, and maintain ETL processes and manage data lakes; at the senior level, perform advanced data lake management.
Implement and manage CI/CD pipelines, using advanced DevOps practices at the senior level.
Create and maintain data models, including advanced models at the senior level, to ensure structured data storage and accessibility.
Manage and enhance data catalogs to organize and provide access to data resources.
Develop, maintain, and optimize data warehouses; at the senior level, oversee complex warehousing and improve efficiency.
Perform system administration and administrative duties; at the senior level, manage complex systems and improve performance, reliability, and ease of management.
Manage data permissions and maintain appropriate access control and data security.
Develop data-driven applications and solutions that support business needs.
Conduct data analysis; at the senior level, lead larger and more advanced analysis projects and provide insights to business stakeholders.
Participate in Agile and Scrum activities; at the senior level, lead Scrum activities and support adherence to Agile practices.
Generate reports using reporting tools and support advanced reporting; at the senior level, automate reports.
Collaborate with stakeholders to gather requirements and define project scope and objectives; at the senior level, align data strategies with business goals.
Develop dashboards that provide visibility into key performance, risk, and control indicators across agency divisions and departments.
Perform related work as assigned.