Senior Data Engineer

Howard University offers an opportunity to build trusted, production-ready data infrastructure that powers institutional analytics, reporting, and strategic decision-making. This on-site role in Washington, DC combines scalable engineering with strong governance practices, helping the university integrate academic and administrative data into datasets teams can rely on.

What you’ll do

The Senior Data Engineer designs, develops, and maintains scalable, secure, and reliable data infrastructure supporting institutional analytics, reporting, data science, and executive reporting. Working under the Chief Data and Analytics Officer, you will integrate data from academic and administrative systems, implement data quality and governance standards, and translate institutional definitions into reliable technical solutions.

• Design, develop, test, and maintain scalable ETL/ELT pipelines integrating data from student information systems, admissions platforms, financial aid systems, ERP systems, APIs, databases, flat files, and other institutional sources.
• Develop and maintain enterprise data models, transformation logic, and standardized datasets for institutional reporting and advanced analytics.
• Build automated pipelines using Python, R, SQL, and PySpark for ingestion, transformation, validation, and delivery of large datasets.
• Design and support cloud-based data lakehouse architectures with data organized into raw, curated, and analytics-ready layers.
• Establish data lineage, metadata, business rules, data dictionaries, and source-to-target mappings to support transparency and data governance.
• Create automated data-quality controls to detect missing, inconsistent, duplicate, or anomalous records and support resolution with data owners and stewards.
• Optimize pipelines, SQL queries, storage structures, and distributed processing workloads for performance, scalability, reliability, and cost efficiency.
• Implement standards for data ownership, access, security, documentation, retention, and quality to support institutional data governance.
• Develop reusable data assets and standardized datasets for dashboards, institutional research, enrollment forecasting, financial modeling, predictive analytics, and executive reporting.
• Implement monitoring, logging, error handling, testing, version control, and documentation to support production-grade data operations.
• Protect sensitive institutional information and support compliance with FERPA, institutional security policies, and applicable data privacy requirements.
• Provide technical guidance and mentorship to analysts, data engineers, and other team members on data engineering standards and best practices.

What you bring

• Advanced proficiency in SQL, Python, and/or R, with experience using PySpark or comparable distributed data-processing technologies.
• Strong knowledge of data modeling, data structures, relational databases, and ETL/ELT pipeline development.
• Familiarity with modern cloud-based data platforms, data lakes, data warehouses, and lakehouse architectures.
• Knowledge of data governance, data lineage, metadata management, data quality, and role-based data access.
• Experience integrating data across APIs, databases, enterprise systems, and structured or semi-structured sources.
• Familiarity with version control, automated testing, CI/CD, and production deployment practices.
• Experience working with higher education data and enterprise systems, including student, enrollment, admissions, financial aid, finance, or HR data.
• Ability to develop reliable, analytics-ready datasets supporting institutional reporting, forecasting, advanced analytics, and machine learning.
• Strong analytical and problem-solving skills, with the ability to independently manage complex data engineering projects.
• Excellent written and verbal communication skills and the ability to collaborate effectively with technical and non-technical stakeholders.

Education and experience: Bachelor’s degree in Data Science, Computer Science, Statistics, Information Systems, Engineering, Quantitative Methods, or a related field, with at least five (5) years of relevant higher education data experience. Master’s degree may qualify with at least three (3) years of relevant higher education data experience.

Salary and benefits

• Compensation Range: $120,000 - $140,000 (annual)
• Insurance: Comprehensive medical, dental, and vision insurance, plus mental health support
• Time off: PTO and paid holidays, with flexible work arrangements
• Retirement: 403(b) with company match
• Growth: Ongoing training, tuition reimbursement, and career advancement paths
• Wellness and perks: Wellness programs and commuter benefits, plus a vibrant company culture

Technologies

Python, R, SQL, PySpark, CI/CD, FERPA