Salary : $132,504.84 - $172,224.00 Annually
Location : 3331 N First St, San Jose, CA 95134, CA
Job Type: Full-Time
Job Number: 26-CK-20214
Division: Finance & Budget
Department: Technology
Opening Date: 08/04/2026
Closing Date: 8/18/2026 11:59 PM Pacific

Job Description
Are you passionate about building modern data platforms that turn complex operational data into trusted insights that improve decision-making?

In this senior technical role, you will design and develop scalable data pipelines, integrate data from complex enterprise systems, build analytical data solutions, and support cloud-based data platform capabilities. You will provide technical leadership, establish best practices, and help shape the future of VTA's enterprise data ecosystem.

The ideal candidate is a seasoned data professional who enjoys solving complex business challenges through modern data engineering practices, data modeling, automation, cloud technologies, and platform optimization. You will collaborate with business leaders, analysts, developers, and technology teams to deliver trusted, high-quality data products that enable better planning, reporting, and operational decision-making.

If you are excited about leading technical initiatives, mentoring others, and building reliable data solutions that create measurable impact for a major public transportation agency, we encourage you to apply.
Definition
Under direction, the Senior Data Warehouse Administrator leads, designs, builds, and maintains data platform and processing systems that combine core data sources into accessible structures to support reporting and analytical systems.
Distinguishing Characteristics
This is the advanced journey-level in Data Warehouse Administrator series. Incumbents in this class work closely with technology staff and business stakeholders to identify appropriate data requirements and develop data solutions that integrate information from multiple systems into well-modeled datasets for reporting and analytics. Incumbents perform highly complex and critical duties, including leading projects and guiding staff in partnership with functional stakeholders. Assigned responsibilities include defining the scope of deliverables, designing and developing solutions, and overseeing the end-to-end delivery of data products to meet analytical needs. Incumbents also perform complex data analysis activities, including conceptualization, modeling, and data presentation to support informed decision-making and planning.
This class differs from the lower-level class of Data Warehouse Administrator II in that the former is an advanced journey level classification and performs the most difficult, complex, and responsible types of duties assigned to this class series, including serving as project lead over other staff.

The Ideal Candidate

The ideal candidate is an experienced data engineering professional who combines deep technical expertise with strong technical leadership and project delivery skills. They have extensive experience designing data models, developing scalable ETL/ELT pipelines, and integrating data from multiple business systems to build enterprise data warehouse and modern data platform solutions. They have successfully delivered analytical solutions, reporting, and business intelligence capabilities that enable data-driven decision-making. They also have experience implementing, administering, and supporting data platform environments, including platform configuration, security and access management, monitoring, troubleshooting, performance optimization, and ongoing operational support.

They have successfully delivered complex data initiatives from concept through implementation while building strong partnerships with business stakeholders, subject matter experts, and cross-functional teams. They are skilled at understanding business and operational needs, translating requirements into sustainable technical solutions, and communicating complex technical concepts to both technical and non-technical audiences. They have experience with modern data architecture, dimensional modeling, cloud data platforms, API integrations, automation, data governance, data quality, and performance optimization.

The successful candidate views data as an enterprise asset and applies best practices for scalability, security, reliability, documentation, and long-term maintainability. They balance technical excellence with business value, demonstrate strong problem-solving skills, mentor team members, lead cross-functional initiatives, and continuously improve enterprise data capabilities to enable data-driven decision-making.

Essential Job Functions
Typical Tasks

• Leads and participates in team efforts in managing projects and developing or enhancing data warehouse and analytical solutions;
• Defines, designs, and owns data transformation and automation processes, and ensures the successful delivery of scalable solutions using a variety of technologies;
• Designs, builds and maintains scalable data pipelines to ingest data into data warehouse from various sources;
• Designs and executes data conversions and manages the import and export of data across internal and external systems, with a focus on reusable frameworks and scalable design;

• Designs and develops data processing systems that integrate core data sources into centralized repositories supporting reporting and analytical functions;
• Identifies and resolves production and/or application development issues related to the database management systems;
• Collaborates with technology staff, business analysts, and other stakeholders to translate business requirements into data workflows;
• Troubleshoots data processing tools, systems, and software applications;
• Performs data validation and reconciliation to ensure data accuracy and integrity;
• Defines performance and capacity standards in alignment with service level agreements, and maintains systems to meet those expectations
• Maintains the quality and integrity of data repositories by adding, modifying, and deleting data in accordance with established policies and business decisions;
• Leads project planning activities and oversees workflows to ensure deliverables are completed within established timelines;
• Identifies and implements improvements to enhance data reliability, efficiency, and quality;
• Leads performance and capacity analysis to support scalability and reliability;
• Establishes and maintains technical documentation, templates and best practices, including data mappings, specifications, data dictionaries, runbooks, and production support materials;
• Designs, develops and maintains application programming interfaces (APIs) to support data accessibility and third-party integration;
• Maintains the data dictionaries and related metadata documentation;
• Supports self-service analytics by publishing certified datasets and reusable pipelines;
• Maintains technical and functional competency in applications utilized by to collect and analyze data in the organization;
• Performs related duties as required.

Minimum Qualifications
Employment Standards

Sufficient education and increasingly responsible experience to demonstrate possession of the required knowledge, skills, and abilities.
Development of the required knowledge, skills, and abilities is typically obtained through a combination of training and experience equivalent to graduation from an accredited college or university with a four-year degree in computer science, engineering, business information systems, management information systems, or a closely related field and a minimum of four (4) years of professional data analytics experience that included designing, implementing, and integrating data analytics and cloud technology components.
Supplemental Information
Knowledge of:

• Extensive knowledge of data processing tools, techniques, and methodologies;
• Expert-level understanding of data warehousing concepts, including ETL vs. ELT, dimensional modeling, and star and snowflake schema design; Modern cloud-based data warehouses;
• Information systems development across the IT lifecycle, with a focus on systems to perform high-volume and high-velocity data processing;
• Modern data modeling techniques, including medallion architecture and data product design;
• Relational and NoSQL databases, including data extraction, querying, and scripting, with experience implementing business logic using SQL-based transformation frameworks'
• Principles and practices of integrating data from Enterprise Resource Planning and Enterprise Asset Management systems into enterprise data platforms;
• Relational database theory, structure, principles, and practices; database normalization concepts, data modeling and performance tuning;
• Software components (e.g., specialized UDFs) and analytics applications;
• Design and development of data pipelines;
• Data governance standards and role-based access control (RBAC) policies;
• Modern data lakes and advanced storage platforms supporting cross-platform data access, handling CDC, and ingesting diverse file formats;
• Basic networking concepts;
• Scripting languages;
• Data visualizations and reporting tools to present information to a variety of audiences using presentation layer tools;
• Data analytics tools, systems, and software troubleshooting;
• Uses source control and CI/CD practices to support version control, collaboration, and efficient development in data engineering and reporting tasks;
• Troubleshooting data systems and issue resolution practices;
• Principles and practices of lead supervision and training;
• Disaster recovery procedures.

Ability to:

• Manage multiple projects involving personnel from multiple departments, as well as consultants and vendors;
• Lead and direct the work of subordinate personnel;
• Train customers, staff and others, as needed;
• Think Data as a product with cost, scalability, and maintainability as a first Principle;
• Design, develop, analyze, troubleshoot, and maintain data engineering solutions, including ETL/ELT processes, data pipelines, automation workflows, and supporting application code;
• Effectively design data pipelines and accurately forecast future data growth;
• Implement statistical modes to identify trends, correlations, and patterns in data sets;
• Support information governance in collaboration with various stakeholders and departmental data stewards;
• Improve and maintain data quality and integrity;
• Catalog and manage data sources;
• Maintain, design, and create relational databases and data systems;
• Design and Build reporting dashboards;
• Utilize presentation tools to communicate technical information effectively;
• Develop and maintain technical documentation records;

• Lead the implementation of data validation and reconciliation processes to ensure data integrity, accuracy, and adhere