Senior Data Engineer

Howard University

6d•$120,000 - $140,000

About The Position

Howard University is hiring a Senior Data Engineer for its new data team. The role designs, develops, and maintains scalable, secure, and reliable data infrastructure supporting institutional analytics, reporting, data science, and strategic decision-making. It integrates academic and administrative data—including admissions, enrollment, student success, financial aid, finance, and human resources—into a governed and dependable enterprise data environment. Reporting to the Chief Data and Analytics Officer, the Senior Data Engineer partners with the Cloud Engineer, Data Governance Manager, technology teams, and functional stakeholders. The position builds production-ready pipelines and data models, establishes data standards, improves data quality, and ensures institutional data is accessible, traceable, documented, and trusted. It also provides technical guidance on data engineering practices.

Requirements

Bachelor’s degree in Data Science, Computer Science, Statistics, Information Systems, Engineering, Quantitative Methods, or a related field, with at least five years of relevant higher education data experience; or a master’s degree in a specified quantitative or related field, with at least three years of relevant higher education data experience.
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 data sources.
Familiarity with version control, automated testing, CI/CD, and production deployment practices.
Ability to develop reliable, analytics-ready datasets supporting institutional reporting, forecasting, advanced analytics, and machine learning.
Strong analytical and problem-solving skills, including the ability to independently manage complex data engineering projects.
Excellent written and verbal communication skills and ability to collaborate with technical and non-technical stakeholders.

Nice To Haves

Experience working with higher education data and enterprise systems, including student, enrollment, admissions, financial aid, finance, or HR data.
A master’s degree in a specified quantitative or related field with at least three years of relevant higher education data experience is identified as preferred.

Responsibilities

Design, develop, test, and maintain scalable ETL/ELT pipelines that integrate 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, structures, transformation logic, and standardized datasets for institutional reporting and advanced analytics.
Build automated pipelines using Python, R, SQL, and PySpark for data ingestion, transformation, validation, and delivery.
Design and support cloud-based data lakehouse architectures, organizing data into raw, curated, and analytics-ready layers.
Maintain data lineage, metadata, business rules, data dictionaries, and source-to-target mappings.
Develop 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 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 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.