Senior Data Analytics Engineer

Vanderbilt University•Nashville, TN

About The Position

The Senior Data Analytics Engineer is part of the Office of Data & Strategic Analytics (DSA) at Vanderbilt University and is a key contributor, utilizing institutionally designated approaches and technologies to ensure successful delivery of high-quality and relevant data solutions to meet consumer needs. Reporting directly to the Director, Data Solutions, the position is responsible for performing complex technical and analytical work across the data and business intelligence lifecycle, including activities that ensure the uniformity, accuracy, and semantic consistency of enterprise data through the application of effective data management, security, stewardship, and governance processes. The Senior Data Analytics Engineer works in partnership with other team members and the broader DSA Team. They are highly proficient in Business Intelligence (BI) tools and possess a strong understanding of university data, applying that knowledge to define data requirements, design data models, build dashboards, develop BI reports, perform ad-hoc analysis, and write SQL to support operational and strategic goals. They provide guidance and mentoring to less experienced analysts in the development and delivery of BI and data solutions. The position also partners closely with information technology to support the development and enhancement of the university’s data warehouse and related metadata and reporting solutions. The position may also employ predictive modeling and advanced analytics techniques to support decision making in areas of strategic interest to the university. About the Work Unit: The Chief Data Officer and the Office of Data and Strategic Analytics report to a new leader for technology and business transformation, who will focus on enhancing capabilities for an AI-enabled future, accelerating innovation, leveraging data strategically, and ensuring Vanderbilt remains at the forefront of technological change. DSA works collaboratively with decision-makers and stakeholders across the university, including in Information Technology, to support the strategic use of data by ensuring people can access relevant data they trust and understand. DSA consists of three teams: Administrative Partnering; Institutional Research & Academic Partnering; and Data Governance, Solutions & Consulting. The Senior Data Analytics Engineer is part of the Data Governance and Solutions team. The Data Governance and Solutions team supports the university's efforts to improve data quality, data governance, and analytics in support of informed data-driven decision-making.

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 Oracle Transactional Business Intelligence (OTBI), Oracle Analytics Cloud (OAC), Tableau, Power BI, or similar tools is necessary.
Advanced SQL skills and experience working with relational databases and cloud data platforms, including Oracle and Snowflake, is necessary.
Experience designing and developing data models to support reporting, analytics, and data products is necessary.
Experience gathering and translating business requirements into technical specifications and data solutions is necessary.
Experience working with large, complex, and 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 alternative approaches, and exercise sound technical judgment is necessary.
Experience providing technical guidance, mentoring, training, or knowledge transfer to other analysts or technical staff is necessary.
Experience leading or managing complex, time-sensitive projects with limited supervision is necessary.

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 within a research-intensive university environment, is preferred.

Responsibilities

Designs, develops, tests, implements, and maintains complex data, reporting, and analytics solutions using institutionally supported technologies, including Oracle Transactional Business Intelligence (OTBI), Oracle Analytics Cloud (OAC), Tableau, Power BI, and Snowflake.
Lead the technical design and development of complex data, reporting, and analytics solutions.
Develop and maintain database tables, views, and other data objects that support reporting and analytics.
Evaluate existing solutions and determine whether they can meet a business need or whether a new solution is appropriate.
Perform testing and validation of 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 ongoing knowledge transfer to other team members.
Translates business and institutional needs into effective, sustainable, and well-documented data and reporting solutions.
Lead requirements gathering with stakeholders to understand business objectives, data needs, business definitions, and intended use.
Translate business requirements into technical specifications and appropriate data solutions.
Design and develop data models to support reporting, analytics, and data products; identify gaps, redundancies, and opportunities to reuse and standardize data structures across applications.
Apply and promote official institutional definitions and DSA standards in reporting and analytics.
Document data sources, data flows, business rules, and other information necessary to support accurate and sustainable solutions.
Present data and analytical findings in a clear, concise, and understandable manner appropriate to the audience.
Applies knowledge of university data and data systems to provide accurate, timely, and meaningful data solutions and to identify and address data quality issues.
Develop an in-depth understanding of the university's functional data environment and apply that knowledge when analyzing complex data questions.
Analyze data and existing solutions to identify inconsistencies, gaps, or potentially problematic data and recommend appropriate solutions.
Collaborate with stakeholders and data partners to improve data quality, consistency, and understanding.
Use analytical techniques to develop test cases and validate reporting, analytics, and data warehouse changes.
Maintain data confidentiality and comply with applicable university policies, laws, and regulations, including FERPA.
Identifies and advances innovative approaches to data, analytics, and business intelligence that improve how DSA delivers value to the university.
Explore and evaluate new technologies, methods, and approaches that have the potential to improve data and analytics capabilities.
Evaluate and apply artificial intelligence, machine learning, and other advanced analytics techniques where appropriate to improve data solutions, automate processes, generate insights, or address emerging institutional needs.
Identify opportunities to simplify, automate, standardize, or improve existing processes and solutions.
Contribute to the development and evolution of new data products and analytics capabilities, including helping define their purpose, structure, functionality, and intended users.
Collaborate with DSA team members, university stakeholders, and Information Technology to develop and test new approaches to delivering trusted data and analytics.
Prototype and evaluate innovative solutions and make recommendations regarding their broader adoption.
Help establish standards, practices, and patterns for emerging data and analytics capabilities as they mature.
Applies data governance principles and institutional standards to promote consistent, well-managed, and appropriately used data.
Maintain and promote consistent business definitions, data standards, and reporting practices.
Maintain metadata, business terms, and other documentation that support understanding and appropriate use of institutional data.
Contribute to federal and external agency reporting requirements.
Assist with the 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 the appropriate, responsible, and secure use of artificial intelligence and advanced analytics with institutional data, consistent with university policies and data governance requirements.