The Senior Data Engineer at Howard University designs, develops, and maintains scalable, secure, and reliable data infrastructure that powers institutional analytics, reporting, data science, and strategic decision-making. This onsite role focuses on integrating academic and administrative data while building production-ready pipelines with governance and data quality built in.

Role Overview

The Senior Data Engineer supports end-to-end institutional data capabilities across Howard University. The position integrates data from academic and administrative systems, develops enterprise data pipelines and models, and implements standards for data quality, governance, security, and documentation. The role reports to the Chief Data and Analytics Officer and partners with technical and functional stakeholders to deliver trusted, analytics-ready datasets, while providing technical guidance on data engineering best practices.

Key Responsibilities

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, data structures, transformation logic, and standardized datasets for 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, organizing data across raw, curated, and analytics-ready layers.
Establish and maintain data lineage, metadata, business rules, data dictionaries, and source-to-target mappings to support transparency and data governance.
Implement automated data-quality controls to identify missing, inconsistent, duplicate, or anomalous records and support issue resolution with data owners and stewards.
Optimize pipelines, SQL queries, storage structures, and distributed processing workloads to improve performance, scalability, reliability, and cost efficiency.
Support institutional data governance by implementing standards related to data ownership, access, security, documentation, retention, and quality.
Create 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 for 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.

Required Qualifications

Advanced proficiency in SQL and 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 for 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.
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 a Master’s degree in one of the listed programs/fields (including Educational Psychology with a Statistics/Quantitative Methods concentration) with at least three (3) years of relevant higher education data experience.

Technologies

Python, R, SQL, PySpark
CI/CD
FERPA

Compensation and Location

Location: Washington, DC (onsite)
Compensation Range: $120,000 - $140,000 per year

Benefits

Comprehensive medical, dental, and vision insurance, plus mental health support
PTO, paid holidays, and flexible work arrangements
Competitive salary and 403(b) with company match
Ongoing training, tuition reimbursement, and career advancement paths
Wellness programs, commuter benefits, and a vibrant company culture

Senior Data Engineer