AI Analytics Engineer

Title:
AI Analytics Engineer

Department:
Analytics and Data Governance (ADG)

Reports To:
Director of Data Analytic

Position Type:
Staff

Position Summary:
The AI Analytics Engineer is responsible for the full lifecycle of data delivery, ranging from rapid-response (Level 1) data requests to deep data cleansing. In this role, you will convert raw institutional data into high-value "AI-ready" assets by engineering the semantic layers required for conversational interfaces and autonomous agents, ensuring these systems-whether purchased or built in-house-are accurately powered and correctly deployed. This role serves as a key technical advisor, making recommendations on whether to buy third-party tools or build custom solutions based on the specific requirements of the data and the desired AI interaction.

As a central technical resource within a tiered data team, you will operationalize the data requirements designed by the Senior Data Scientist/AI Engineer while ensuring strict adherence to the institutional security frameworks and governance standards established by the Data Governance Lead.

Essential Functions:
-Works as a central technical resource within the Analytics and Data Governance team to ensure data integrity and model readiness. This includes:
-Primary technical partner for the Senior Data Scientist and AI Engineer: Build and automate the data features and training sets required for predictive models, analytical tools, and AI models. Manage the technical relationship with AI vendors under the direction of the Director, ensuring appropriate deployment and integration of purchased tools. Actively evaluate and recommend "buy vs. build" strategies to ADG, identifying where purchased tools meet institutional needs and where unique data complexities require supplemental in-house engineering.
-Operationalize governance standards established by the Data Governance Lead by applying approved data definitions, classification rules, access controls, and security requirements within transformation logic, semantic layers, and downstream data products. Maintain technical lineage and metadata needed to support compliant, governed, and reusable institutional data assets.
-Serve as the primary technical liaison between the Office of Institutional Effectiveness, Business Intelligence, Integrations, and Digital Learning teams to ensure official reporting data and AI model outputs are seamlessly integrated into dashboards and applications, while documenting the transformation logic and technical dependencies required to maintain data integrity across all internal analytics use cases.
-Manage the standardized intake process for Level 1 data requests by extracting, assembling, and delivering datasets from the data warehouse (Snowflake) in requested formats.
-Develop practical tools, examples, and "how-to" documentation that help campus users access and use governed data correctly, supporting the transition from basic data requests to responsible self-service analytics and AI-assisted workflows.
-Design and maintain governed semantic layers that provide a consistent, well-documented source of business meaning for both human-led BI and autonomous AI agent consumption.
-Build and configure data interfaces (e.g., API endpoints or Model Context Protocols) that allow AI systems to reliably query institutional data. Manage the technical deployment and configuration of AI interaction layers to ensure appropriate integration with these interfaces.
-Support lightweight Machine Learning Operations (MLOps) tasks including routine deployments, versioning, and API integration into application pipelines, ensuring complex architecture or infrastructure issues are escalated to the Senior Data Scientist/AI Engineer.
-Perform extraction, transformation, cleansing, and documentation of datasets to ensure usability and clear lineage for downstream teams and AI use cases, coordinating with domain stewards to ensure all governed data meets institutional requirements.
-Maintain all transformation logic and modeling code using version control (e.g., Git, Snowflake), ensuring a clearly documented and reproducible technical data catalog.
-Implement automated testing, validation, and data quality checks to verify that data is accurate, consistent, secure, and reproducible across reports, semantic layers, and AI/ML data assets.

Additional Functions:
-Support data and AI literacy efforts by creating role-appropriate guidance, examples, and usage guardrails that help campus users interpret, access, and use governed data responsibly.
-Keeps abreast of emerging technology trends in the modern data stack and the AI vendor landscape and takes an active interest in professional development to ensure skills stay current.
-Identify recurring data definition conflicts, usage inconsistencies, and business-process issues encountered in technical implementation, and escalate them to the Data Governance Lead and domain stewards for resolution.

Prerequisite Qualifications:
-A Master's degree in Computer Science, Data Science, Software Engineering, Artificial Intelligence, or a related field.
-Advanced proficiency in SQL and experience with cloud data warehouses (e.g., Snowflake).
-Programming ability in Python or R for data manipulation and pipeline automation.
-Experience with data manipulation libraries (e.g., NumPy, Pandas) and version control (e.g., Git, Snowflake).
-Proficiency with data visualization tools (e.g., Strategy, Tableau).
-Demonstrated ability to evaluate technical trade-offs and provide recommendations on software/tool selection.
-Professional experience applying machine learning and statistical methods to real-world data, including the cleaning and preprocessing of large, complex datasets.
-Demonstrated experience working with business domain stakeholders to interpret metrics and translate technical concepts into actionable business logic.
-At the university's discretion, the education and experience prerequisites may be exempted where the candidate can demonstrate to the satisfaction of the university, an equivalent combination of education and experience specifically preparing the candidate for success in the position. Preferred Qualifications:
-At least three years of professional experience in data analytics, data engineering, or software engineering.
-Working knowledge of data quality concepts, lineage, metadata management, and institutional governance processes.
-Experience working with higher education ERP systems (e.g., Workday, Banner).
-Experience in a higher education or research-intensive university setting.
-Awareness of FERPA, GLBA, and responsible AI practices.
-Familiarity with AI orchestration frameworks (e.g., LangChain).
-Familiarity with project management frameworks in a technical environment.

Bargaining Unit:
PSA

Range/Band:
26

Salary Information:
In compliance with the NJ Pay Transparency Law, the negotiated annual salary range for this position is $73,502.29-$137,773.44 (USD). NJIT considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/training, key skills, internal peer equity, as well as, market and organizational considerations when extending an offer. This pay range represents base pay only and excludes any additional items such as incentives, bonuses or other items.

To learn more about the comprehensive benefits NJIT offers for this position, please visit our benefits page: https://hr.njit.edu/health-benefits.

FLSA:
Exempt
Full-Time