Analytics Engineer

Analytics Engineer Analytics Exempt Regular 3.2

Location:

Adelphi, MD The Analytics Engineer builds and maintains the analytics-ready layers of the University's Databricks Lakehouse, transforming curated data into business-ready models and contributing to the semantic layer that defines institutional metrics. Working within established modeling standards, this role delivers data products that support traditional reporting, AI-powered analytics, and natural language query capabilities, and helps maintain legacy reporting environments. Strong SQL and Python proficiency are required.

Build and maintain Gold and Platinum layer data models in Databricks following established design patterns, transforming curated data into business-ready and consumption-ready objects using SQL and Python, orchestrated through Databricks Workflows and declarative pipelines. Develop and maintain semantic layer objects using Databricks metric views, implementing approved institutional metric definitions and documenting them clearly for business consumers. Prepare and maintain the datasets, metadata, descriptions, and example queries that support Databricks AI Genie Agents and AI/BI dashboards;

test agent responses against expected results and elevate issues requiring model changes. Test, validate, and reconcile modeled results against systems of record; investigate discrepancies, document findings, and maintain data lineage and catalog documentation in Unity Catalog. Maintain, troubleshoot, and optimize Amazon Redshift reporting assets, supporting ongoing business needs and migration initiatives to Databricks. Support analytical and reporting requirements through ad hoc SQL development, data validation activities, and Tableau dashboard development and maintenance.

Other duties as assigned.

Analytical Thinking, Quality & Accuracy, Data Governance & Stewardship, Problem Solving, Collaboration & Communication, Continuous Learning, Customer Focus, Accountability & Follow-Through.

Strong SQL, including joins, aggregations, window functions, common table expressions (CTEs), and query troubleshooting. Python programming for data transformation, automation, and testing using PySpark and/or pandas. Working knowledge of Databricks including Delta Lake, Databricks SQL, notebooks, Workflows, and Medallion Architecture. Dimensional data modeling fundamentals, including star schema concepts, fact and dimension tables, and definition of grain and keys. Familiarity with semantic and metric layer concepts and documenting metric definitions.

Amazon Redshift SQL development and troubleshooting. Tableau development and dashboard design. Git-based version control and development practices. Ability to interpret business requirements, ask clarifying questions, and communicate technical concepts to non-technical audiences.

Education:

Bachelor's Degree Computer Science, Information Systems, Data Analytics, Statistics, Mathematics, or a related field.

Experience:

Three (3) to five (5) years of experience building and maintaining production data models, datasets, or reporting assets consumed by business intelligence tools and business users. Experience writing production SQL against a cloud data warehouse or Lakehouse platform. Experience working within an established data architecture and following defined modeling, testing, and documentation standards.

N/A

Education:

Bachelor's Degree.

Experience:

Hands‑on experience with Databricks, including Delta Lake, Databricks SQL, and Workflows within a Medallion Architecture. Experience contributing to semantic or metric layer development. Exposure to AI‑enabled or…