Lead Analytics Engineer

Salary

Budgeted Salary: $130,000-$170,700/annually. Salary commensurate with skills, qualifications and experience.

The Office of Budget Analysis and Planning’s Data Management team oversees and supports campus data warehouse services in partnership with Information Technology Services. For almost 30 years, the campus data warehouse has grown and matured in response to campus-wide reporting needs across 35+ subject areas from 50+ data sources. Reports available through the reporting tool InfoView (SAP Business Objects), both standard and ad hoc, are widely used on campus for both daily operational information and longitudinal trend analysis. The topical areas of reporting are varied and include enterprise system reporting, such as financial operations, personnel, payroll, academic information, philanthropic donations, campus facilities, etc., as well as unit-specific operations such as dining point of sale, purchasing turn-time, equipment management, etc. In 2022, the unit co-sponsored and funded the transformative initiative to modernize the data warehouse and implement a cloud-based common data platform.

UC Santa Cruz has launched a multi-year strategic initiative with the objectives of modernizing the existing campus data warehouse and streamlining data integration with and across campus enterprise systems. The Common Data Platform (CDP) initiative is sponsored by campus leadership to ensure the campus' data assets support the mission and vision of the University. The CDP will transform the information experience, empower business units, and provide stewardship over shared campus resources to support student and employee success. Analytics Engineers sit at the intersection of business units and data engineering and are responsible for bringing robust, efficient, and integrated data models and products to life. Analytics Engineers speak the language of business units and technical teams, translating data insights and analysis needs into models powered by the CDP. Successful Analytics Engineers can blend business process understanding with technical expertise and transition between business processes and data development. Under the general supervision of the Director of Data Management, the Lead Analytics Engineer has broad responsibilities across the project team and data stack. In addition to building partner relationships with campus constituencies, delivering trusted, curated, accurate data sets, semantic modeling, and data integrations to meet the information needs of the campus. The Lead Analytics Engineer is responsible for developing and maintaining standards, style guides and processes to be followed campus-wide. This position will also have supervisory responsibilities for Analytics Engineers.

Job Duties

50%% - Product and Services Management

Leads small to medium-sized complex projects with cross-functional teams to build trust in the processes, standards, and, ultimately, the data products that support campus information needs.
Collaborating with team members and business partners to collect business requirements, define successful analytics and information outcomes, and design data models in support.
Transforming raw data into well-designed, documented, tested data models and maintaining these models over time, keeping pace with the organization's dynamic needs.
Adopting software engineering best practices like version control, unit tests, and in-line documentation to develop robust data models and data marts in collaboration with business partners and stakeholders. Maintains models in the event of unforeseen disruptions in data pipelines.
Working with various business intelligence (BI) tools to promote information delivery through dashboards, reporting, and data sharing and support users of same.
Monitoring and communicating project status to the appropriate individuals.
Leading code reviews and pull request sessions with stakeholders, partners, and other interested parties, ensuring standards and style guide principles were followed.
Champions the adoption of an agile development methodology, collaboratively manages product backlog, and organizes workload for the team through sprints.

25%% - Team Lead Supervisor

Provides supervision and leadership for direct reports and fosters collaboration through mentorship and support to campus units interested in collaborative development through analytics engineering.
Activities include recruiting, onboarding, training new team members, retention activities, performance management, and maintaining accurate employment records, including personnel policies and procedures.
Training and onboarding new hires to ensure they understand their roles and are set up for success. Identifying the right training opportunities to achieve positive outcomes.
Collaboratively developing standards for lifecycle development, deployment, and continuous integration.
Oversee production operations for CDP.

25%% - Champions the role of Analytics Engineer

Develops mentorship program for project members and campus constituents interested in analytics engineering.
Supports the onboarding of business partners into the dbt-cloud environment, promoting collaboration and standard processes and necessary training.
Collaboratively develop subject matter expertise and domain knowledge through business partner engagement.

Required Qualifications

Bachelor's degree in related area and/or equivalent experience/training
Experience mentoring and developing team members.
Demonstrated skill in managing technical staff.
Strong knowledge of human resources policies and procedures relating to supervisory responsibilities.
Knowledge of business analysis processes and procedures and alignment and compliance with applicable policy and regulation.
Experience building trusted partnerships across departments and business units.
Demonstrated effective oral communication skills with the demonstrated ability to communicate technical information to technical and non-technical personnel at various organizational levels.
Excellent written communication skills.
Demonstrated interpersonal skills.
Demonstrated hands-on engagement, such as performing analysis or writing complex queries and models.
Excellent analytical and quantitative skills.
Excellent critical thinking and problem solving skills.
Ability to lead and support the adoption of agile development methodology. Experience managing workload in an agile methodology, such as leading product sprints, organizing product backlogs, and preparing minimal viable products.
Skillful and creative in troubleshooting information processes, data analysis, data lineage, and identification of data quality issues in relation to assigned responsibilities.
Ability to evaluate data and analytically based problems to determine the best solutions for clients and stakeholders.
Knowledge and experience with business analysis techniques and compliance with applicable policy and regulation, such as data security best practices and appropriate use of information, including the handling of confidential and restricted data (“FERPA”,”PII”, etc.) as well as information security practices.
Advanced knowledge of information architecture, including common data structures, models, design, and processing methodologies.
Experience with version control platforms such as Gitlab or GitHub

Preferred Qualifications

Experience with dimensional modeling concepts or similar approaches in producing useful information for analysis and reporting.
Experience with cloud data warehousing technologies associated with the modern data stack.
Two or more years using dbt to deliver and maintain clean, transformed data models ready for analytics

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