Analytics Engineer

The Analytics Engineer is the technical architect responsible for evolving Common App’s shared data warehouse, semantic layer, and reusable data assets to enable scalable analytics, data governance, and artificial intelligence readiness across the organization. This role is central to operationalizing governance standards within enterprise systems and building the shared data foundations that power the Applied Analytics and Research team.

Reporting to the Senior Director, Analytics Engineering, the Analytics Engineer partners with Data Engineering and Applied Analytics and Research to deliver insight-ready data, and with Data Governance to ensure that governance rules and standards are technically enforceable within the enterprise data platform. This role focuses on developing Common App’s reporting schema, refactoring existing tables and pipelines for performance, cost efficiency, and maintainability; designing and implementing automated data quality frameworks; establishing monitoring and alerting for critical datasets and pipelines; maintaining warehouse data documentation; and architecting data products that structure the platform for scalability, quality, and self-service.

The Analytics Engineer is a recognized subject matter expert in data platform engineering who combines deep data modeling expertise with a platform mindset, delivers outstanding results, and drives innovation in how Common App's shared data infrastructure is designed and governed.

Requirements

QUALIFICATIONS

This role requires:

• Candidates must live in the United States.
• Willing to travel to attend twice annual Common App Retreat.
• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Statistics, or a closely related quantitative or technical field; or an equivalent combination of education and experience.
• 5-8 years of progressive experience in analytics engineering, data engineering, or a closely related data platform role.
• Demonstrated experience working within an enterprise data warehouse environment at scale, including ownership of data models, transformation pipelines, and data quality processes.
• Experience implementing data governance requirements — such as retention, deletion, or PII controls — within a production data platform.
• Strong expertise in SQL and data modeling, including dimensional modeling, star/snowflake schemas, and modern data transformation approaches.
• Demonstrated experience designing, building, and maintaining data pipelines and transformation layers within cloud data warehouse platforms (e.g., Databricks, Snowflake, BigQuery, or equivalent).
• Experience with Databricks-specific features, including Unity Catalog, Delta Live Tables, or Databricks workflows.
• Experience designing and maintaining a semantic layer or metrics layer that serves multiple downstream consumers, including BI tools, analysts, and AI systems.
• Demonstrated thought leadership in data platform engineering, with a track record of innovating and contributing to the advancement of analytics engineering practices within or beyond the organization.
• Proven ability to contribute to long-term platform planning and influence organizational data strategies through technical expertise and cross-functional engagement.
• Demonstrated experience implementing automated data quality frameworks, including pipeline-embedded tests, validation logic, and monitoring/alerting for production datasets.
• Understanding of data governance concepts and experience translating governance requirements – including data retention, PII handling, and access controls – into technical implementations within data platforms.
• Proficiency in Python for data engineering and analytics workloads; familiarity with orchestration tools (e.g., Apache Airflow, dbt Cloud, or Databricks AI/BI).
• Experience designing and implementing data products and reusable data assets for self-service analytic consumption.
• Strong documentation practices, with the ability to create and maintain clear, accurate data dictionaries, lineage records, and model descriptions.
• Strong interpersonal and communication skills, with the ability to collaborate effectively with analysts, researchers, data scientists, and governance stakeholders of varying technical backgrounds.
• Strong collaboration and influencing skills, with the ability to build trusted relationships with stakeholders across technical and non-technical functions and drive outcomes without formal authority.
• Proficiency in collaboration and version control tools (GitHub, Git).

The ideal candidate will possess:

• Advanced training and experience in Tableau for semantic layer integration and BI dashboard development.
• Familiarity with data catalog and metadata management tools (e.g., Alation, Atlan, or similar).
• Experience working with Agile frameworks for cross-functional, product-minded collaboration.
• Experience in the higher education, nonprofit, or mission-driven technology sector.
• Relevant data engineering or cloud platform certifications (e.g., Databricks Certified Data Engineer, dbt Certified Developer, or equivalent).