Senior Data Engineer

The Senior Data Engineer is responsible for designing, developing, and maintaining scalable data infrastructure that supports institutional analytics, reporting, data science, and strategic decision-making. This position works across academic and administrative data domains, including admissions, enrollment, student success, financial aid, finance, human resources, and other institutional systems, to integrate data into a secure, governed, and reliable enterprise data environment. The Senior Data Engineer will report to the Chief Data and Analytics Officer, partner with Cloud Engineer, Data Governance Manager, and technology teams to build production-ready data pipelines, establish data standards, improve data quality, and ensure institutional data is accessible, traceable, and trusted., The Senior Data Engineer designs, develops, and maintains scalable, secure, and reliable data infrastructure supporting institutional analytics, reporting, data science, and strategic decision-making across Howard University. The position integrates data from academic and administrative systems, develops enterprise data pipelines and models, and implements data quality, governance, security, and documentation standards. Reporting to the Chief Data and Analytics Officer, the Senior Data Engineer collaborates with technical and functional stakeholders to deliver trusted, analytics-ready data and provides technical guidance on data engineering best practices., * Design, develop, test, and maintain scalable ETL/ELT pipelines integrating data from the student information systems, admissions platforms, financial aid systems, ERP systems, APIs, databases, flat files, and other institutional sources.

• Develop and maintain enterprise data models, data structures, transformation logic, and standardized datasets supporting institutional reporting and advanced analytics.
• Build automated data pipelines using Python, R, SQL, and PySpark for ingestion, transformation, validation, and delivery of large and complex datasets.
• Design and support cloud-based data lakehouse architectures and organize data across raw, curated, and analytics-ready layers.
• Establish and maintain data lineage, metadata, business rules, data dictionaries, and source-to-target mappings to improve transparency and data governance.
• Develop automated data-quality controls to identify missing, inconsistent, duplicate, or anomalous records and support issue resolution with data owners and stewards.
• Optimize data pipelines, SQL queries, storage structures, and distributed processing workloads for performance, scalability, reliability, and cost efficiency.
• Support institutional data governance by implementing standards for data ownership, access, security, documentation, retention, and quality.
• Develop reusable data assets and standardized datasets for dashboards, institutional research, enrollment forecasting, financial modeling, predictive analytics, and executive reporting.
• Collaborate with functional offices to translate business requirements and institutional definitions into reliable technical solutions.
• Implement pipeline 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.

• 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/semi-structured data 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.

MINIMUM REQUIREMENTS:

• 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; or Master’s degree in Data Science, Educational Psychology with a Statistics/Quantitative Methods concentration, Quantitative Measurement, Statistics, Computer Science, Information Systems, or a related quantitative field, with at least three (3) year of relevant higher education data experience is preferred. *

• Health & Wellness: Comprehensive medical, dental, and vision insurance, plus mental health support
• Work-Life Balance: PTO, paid holidays, flexible work arrangements
• Financial Wellness: Competitive salary, 403(b) with company match
• Professional Development: Ongoing training, tuition reimbursement, and career advancement paths
• Additional Perks: Wellness programs, commuter benefits, and a vibrant company culture, Compensation Range: $120,000 - $140,000

The Talent Acquisition department hires qualified candidates to fill positions which contribute to the overall strategic success of Howard University. Hiring staff “for fit” makes significant contributions to Howard University’s overall mission.