Senior Analytics Engineer

Senior Analytics Engineer Analytics Exempt Regular 3.4

Location:

Adelphi, MD

The Senior Analytics Engineer owns the analytics-ready layers of the University's Databricks Lakehouse, transforming curated data into business-ready models and establishing the semantic layer that defines authoritative institutional metrics. This role sets the modeling standards the analytics team builds against, partners directly with business stakeholders to define and govern metrics, and leads the data work behind traditional reporting, AI-powered analytics, and natural language query capabilities.

Advanced SQL and Python proficiency are required.

• Architect, build, and maintain Gold and Platinum layer data models in Databricks, establishing the design patterns, grain, and business logic the team builds against, and optimizing transformations for performance and cost.
• Own and govern the semantic layer using Databricks metric views, establishing authoritative institutional metric definitions in partnership with business stakeholders and managing version control and downstream impact as those definitions change.
• Lead the curation of datasets, metric views, metadata, instructions, and certified SQL examples that support Databricks AI Genie Agents and AI/BI dashboards; evaluate agent accuracy and drive improvements through model and semantic changes.
• Establish and administer data quality, validation, and reconciliation practices against systems of record; resolve discrepancies to root cause; and define governance, lineage, and access standards through Unity Catalog.
• Maintain and optimize Amazon Redshift reporting assets, determine the appropriate platform for new development, and lead migration of legacy assets to Databricks.
• Provide technical leadership through code review, modeling standards, and mentoring of analytics staff, while supporting analytical and reporting requirements through ad hoc SQL development, data validation, and Tableau development as needed.
• Other duties as assigned.

Competencies:

Analytical Thinking Technical Leadership & Mentoring Data Governance & Stewardship Business Partnership Problem Solving Continuous Improvement Communication & Influence Quality & Accuracy

Skills:

Advanced SQL, including complex joins, window functions, common table expressions (CTEs), incremental processing, and performance tuning. Python programming for data transformation, automation, and testing using PySpark and/or pandas. Databricks expertise including Delta Lake, Databricks SQL, notebooks, Workflows, declarative pipelines, Unity Catalog, and Medallion Architecture. Semantic and metric layer design, governance, and change management. Dimensional data modeling, star schema design, slowly changing dimensions, and metric development.

Amazon Redshift administration and optimization. Tableau development and dashboard design. Git-based version control and development practices. Natural language analytics and AI-enabled data platforms. Establishing modeling standards, naming conventions, and code review practices for a team. Ability to translate business requirements into scalable data solutions and communicate technical concepts to non-technical and executive audiences.

Education & Experience Requirements :

Education:

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

Experience:

Seven (7) to ten (10) years of experience in data, business intelligence, or analytics disciplines, including five (5) or more years building and maintaining production data models and datasets consumed by business intelligence tools and business users in a cloud data warehouse or Lakehouse environment. Experience developing, governing, and maintaining semantic or metric layers supporting enterprise reporting and analytics. Experience with Databricks, SQL, Python, dimensional data modeling, and data governance practices.

Experience establishing modeling standards, conducting code review, and mentoring data professionals.

Education:

Master's Degree

Experience:

Experience with Databricks metric views and supporting Databricks AI Genie Agents or similar natural language analytics platforms. Experience migrating legacy data warehouse environments, including Amazon Redshift, to modern Lakehouse architectures. Higher education experience supporting enrollment, retention, student lifecycle, academic operations, or related functions.

Experience with People Soft, D2L,…