Senior Data Analytics Engineer

Vanderbilt University•Nashville, TN

About The Position

The Senior Data Analytics Engineer is a key contributor in Vanderbilt University’s Office of Data & Strategic Analytics (DSA), within the Data Governance and Solutions team. The role performs complex technical and analytical work across the data and business intelligence lifecycle, ensuring enterprise data is uniform, accurate, and semantically consistent through effective data management, security, stewardship, and governance practices. Working with DSA colleagues, university stakeholders, and Information Technology, the engineer translates business needs into data requirements and technical specifications; designs data models; develops, tests, and maintains data, reporting, and analytics solutions; builds dashboards and BI reports; writes SQL; and performs ad hoc analysis. The position provides technical guidance and mentoring to less experienced analysts and supports development of the university data warehouse, metadata, and reporting solutions. It may also apply predictive modeling and advanced analytics to support institutional decision-making. The role uses institutionally supported technologies, including Oracle Transactional Business Intelligence (OTBI), Oracle Analytics Cloud (OAC), Tableau, Power BI, and Snowflake. It also supports data quality, institutional reporting, data governance, and responsible use of artificial intelligence and advanced analytics with institutional data. The position reports to the Director of Data Solutions and has no supervisory responsibilities.

Requirements

Bachelor’s degree in computer science, business analytics, or related experience is necessary.
Experience developing complex data, reporting, and analytics solutions using BI and analytics technologies such as OTBI, OAC, Tableau, Power BI, or similar tools is necessary.
Advanced SQL skills and experience with relational databases and cloud data platforms, including Oracle and Snowflake, are necessary.
Experience designing and developing data models for reporting, analytics, and data products is necessary.
Experience gathering and translating business requirements into technical specifications and data solutions is necessary.
Experience with large, complex, integrated datasets and identifying data quality issues, inconsistencies, and gaps is necessary.
Experience using data transformation, analytical, or programming tools such as Python, R, Excel, or similar technologies is necessary.
Experience testing and validating data, reporting, analytics, and data warehouse solutions is necessary.
Experience applying data governance principles, including data definitions, metadata, documentation, data quality, security, and appropriate use of institutional data, is necessary.
Demonstrated ability to independently analyze complex problems, evaluate alternatives, and exercise sound technical judgment is necessary.
Experience providing technical guidance, mentoring, training, or knowledge transfer to analysts or technical staff is necessary.
Experience leading or managing complex, time-sensitive projects with limited supervision is necessary.
Demonstrates attention to detail, thoroughness, quality assurance, and responsible handling of mistakes.
Demonstrates ethical judgment and respect for the confidentiality, privacy, and dignity of personal data.
Values teamwork and communicates proactively with teammates, leaders, and institutional stakeholders.
Demonstrates creative problem-solving, proactive learning, adaptability, thoughtful listening, civil discourse, self-discipline, and accountability.
Supports the value of higher education and understands the distinctive values of a research-intensive, highly selective private institution.

Nice To Haves

Experience evaluating or applying emerging technologies, including artificial intelligence, machine learning, or advanced analytics, is preferred.
Experience working in higher education, particularly in a research-intensive university environment, is preferred.

Responsibilities

Lead the technical design and development of complex data, reporting, and analytics solutions using institutionally supported technologies.
Develop and maintain database tables, views, and other data objects that support reporting and analytics.
Evaluate existing solutions against business needs and determine whether to adapt them or develop a new solution.
Test and validate data and BI solutions, data changes, tool upgrades, enhancements, and change requests.
Identify opportunities to improve the efficiency, consistency, usability, and maintainability of data and BI solutions.
Provide technical guidance, training, and knowledge transfer to team members.
Lead requirements gathering with stakeholders and translate business needs into technical specifications and sustainable data solutions.
Design and develop data models for reporting, analytics, and data products; identify gaps, redundancies, and opportunities to reuse and standardize data structures.
Apply institutional definitions and DSA standards in reporting and analytics, and document data sources, data flows, business rules, and related solution information.
Present data and analytical findings clearly and appropriately for the audience.
Develop an in-depth understanding of university data and systems and apply it to complex data questions.
Analyze data and solutions to identify inconsistencies, gaps, and potential data problems; recommend solutions and collaborate with partners to improve data quality and understanding.
Develop test cases and validate reporting, analytics, and data warehouse changes.
Maintain data confidentiality and comply with university policies, laws, and regulations, including FERPA.
Explore and evaluate technologies, methods, and approaches that could improve data and analytics capabilities.
Evaluate and apply AI, machine learning, and other advanced analytics techniques where appropriate to improve solutions, automate processes, generate insights, or address institutional needs.
Identify opportunities to simplify, automate, standardize, or improve processes and solutions.
Contribute to new data products and analytics capabilities, including defining their purpose, structure, functionality, and intended users.
Collaborate with DSA colleagues, university stakeholders, and Information Technology to develop and test approaches for delivering trusted data and analytics.
Prototype and evaluate innovative solutions, recommend broader adoption, and help establish standards and practices for emerging capabilities.
Maintain and promote consistent business definitions, data standards, reporting practices, metadata, business terms, and documentation.
Contribute to federal and external agency reporting requirements and assist with annual development and validation of publicly available institutional data, including the VU Factbook and Common Data Set.
Promote appropriate data stewardship, security, confidentiality, and governance practices in the development and use of data solutions.
Evaluate responsible and secure use of AI and advanced analytics with institutional data in accordance with university policies and governance requirements.