Senior Data Engineer

About the Role

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines integrating data from diverse internal and external sources.
• Develop and optimize SQL-based processes to support data loading, transformation, and validation tasks.
• Architect and implement cloud-based data solutions using platforms such as AWS or Azure.
• Maintain and enhance KU's Data Warehouse and Data Lake environments to support analytics, dashboards, and operational reports.
• Support AI/ML model integration pipelines and prepare data for model training, scoring, and inferencing.
• Collaborate with BI Analysts, Data Scientists, and business stakeholders to translate requirements into performant data engineering solutions.
• Build and maintain APIs and web services for data access, exchange, and automation.
• Maintain and document data workflows, source-to-target mappings, and architecture diagrams in accordance with data governance policies.
• Provide production support for ETL/ELT jobs, data pipelines, and cloud-based infrastructure.
• Troubleshoot issues related to data ingestion, transformation, and availability.
• Monitor job performance and system health, coordinating with IT operations as needed.
• Participate in upgrade cycles for ETL tools, cloud platforms, and database systems.
• Support version control, code deployment, and CI/CD practices for data applications.
• Mentor junior data engineers and guide technical implementation best practices.
• Lead code reviews and drive architectural decisions for data projects.
• Define and execute data validation strategies for all data pipelines and models.
• Implement audit controls, data quality checks, and reconciliation procedures.
• Participate in campus-wide data initiatives, pilot projects, and tool evaluations.

Requirements

• Bachelor's degree in computer science, Information Technology, Engineering, Mathematics, Statistics or a related field and ten (10) years of relevant professional experience OR Master's degree with eight (8) years of experience.
• Over ten (10) years of hands-on experience with SQL and database systems (e.g., Oracle, PostgreSQL, SQL Server, MySQL).
• Experience in data warehousing and data lake architectures.
• Over eight (8) years of experience using Python for scripting, automation, and data manipulation across diverse projects and environments.
• Experience developing scalable ETL/ELT pipelines.
• Hands-on experience with cloud platforms (AWS, Azure, or GCP).
• Experience working with APIs and web services for data integration.
• Over five (5) years of experience in data modeling, validation, and applying data governance principles to ensure data integrity and compliance.

Nice to Have

• Experience integrating AI/ML workflows into data pipelines.
• Experience with data cataloging and metadata management tools.
• Experience with data security, access control, and compliance frameworks.
• Experience working in an Agile environment with CI/CD tools.
• Excellent communication, documentation, and collaboration skills.

Qualifications

• Formal degree in computer science, Information Technology, Engineering, Mathematics, Statistics or a related field.

Benefits & Perks

• Salary starting at $110,000, dependent upon experience.
• Work schedule of Monday to Friday, 8 am to 5 pm.
• Hybrid work location arrangement.