Application Data Engineer

Role: Application Data Engineer 

Location -  Albany, NY 12246 (Onsite)

Duration: Long Term

Description 

The State University of New York (SUNY) seeks to award a single contract to one bidder that will fulfill the role of Application Data Engineer - Student Systems. This role will focus on supporting the technical and engineering needs of student systems, with the Unified Student Information Management (SIM) System being a critical priority. Additionally, the engineer will support other student systems across the SUNY network. The successful candidate will be responsible for designing and maintaining data pipelines, managing Extract, Transform, and Load (ETL) processes, and ensuring efficient data integration and functionality. They will collaborate with the Project Management Office (PMO) and project leaders to enhance workflows and leverage project management tools, contributing to the success of both the Unified SIM System and other initiatives. 

Mandatory Required Experience:

• 12+ Years in Data Engineering and Student Systems 
• Over a decade of hands-on experience designing, implementing, and optimizing data pipelines and integration architectures. 
• Significant tenure working with education clients (universities, community colleges) and/or large government or corporate entities on mission-critical data initiatives. 
• Proven track record of contributing to large-scale Student Information System (SIS) projects, Learning Management System (LMS) integrations, and other student-focused platforms. 
• Experience in designing and maintaining data pipelines, managing Extract, Transform, and Load (ETL) processes, and ensuring efficient data integration and functionality. 
• At least 10 years of experience in education, government, or corporate projects, with expertise in application development and data system design preferred. 
• Proven ability to provide strategic advice to senior management and improve business processes and systems for greater efficiency and effectiveness. 
• Has served in both technical and advisory capacities, providing guidance to senior leadership on data strategy, governance, and compliance. 
• Led cross-functional teams to improve business processes, enhance data quality, and optimize system performance for improved student and faculty experiences.

Key Responsibilities 

The Application Data Engineer - Student Systems will perform a range of technical and advisory functions aimed at supporting and improving student-focused applications and systems. 

Key responsibilities include: 

● Data Integration and Pipeline Development: 

○ Design and develop scalable data pipelines to process and analyze large datasets. o Implement data integration solutions using ETL tools and frameworks. 

○ Monitor, troubleshoot, and optimize data flows to ensure reliability, quality, and integrity. 

○ Develop and maintain documentation for data models and integration processes. 

● Collaboration and Strategy: 

○ Partner with application developers, data scientists, and business analysts to align data integration efforts with student information systems (SIS), learning management systems (LMS), and other related platforms. 

○ Engage with the Project Management Office (PMO) to identify areas for improvement and ensure compliance with project management tools and methodologies. 

● Innovation and Compliance: 

○ Stay current with emerging data technologies and best practices. 

○ Ensure data security and adherence to relevant regulatory standards. o Design & Develop solutions that enhance the student experience and streamline administrative processes.

Mandatory Key Skills 

Key Skills & Qualifications 

1. Data Pipeline & Integration Expertise 

• ETL Development: Proficient with leading ETL tools (e.g., Talend, Informatica, SSIS) and modern orchestrators (e.g., Apache Airflow, Azure Data Factory). 
• Data Modeling: Skilled in designing relational and dimensional data models to ensure accuracy, scalability, and efficient querying across student, financial, and administrative datasets. 
• Real-Time Streaming: Familiar with technologies such as Apache Kafka and AWS Kinesis for near real-time data ingestion and event-driven architectures. 

2. Technical Proficiencies 

• Programming Languages: Python, SQL, Java, or Scala for data transformation, scripting, and automation. 
• Cloud & On-Premises Environments: Experience deploying solutions on AWS, Azure, or on-premises data centers, leveraging containerization (Docker/Kubernetes) when needed. 
• Database Systems: Background in working with both SQL (Oracle, SQL Server, PostgreSQL) and NoSQL (MongoDB, Cassandra) databases. 
• DevOps & CI/CD: Understanding of CI/CD pipelines, Git-based version control, and automated testing frameworks to streamline development and deployment. 

3. Higher Education Domain Knowledge 

• Student Information Systems (SIS): In-depth familiarity with leading SIS platforms (e.g., Banner, PeopleSoft, Workday) and the data flows integral to student admissions, enrollment, registrar functions, and financial aid. 
• Learning Management Systems (LMS): Hands-on integration experience with LMS solutions like Blackboard, Canvas, or Moodle, ensuring seamless data exchange between academic and administrative platforms. 
• Regulatory Compliance (FERPA): Thorough understanding of FERPA guidelines and other federal/state mandates relating to student data privacy, including the design of secure data handling procedures. 

4. Project Management & Collaboration 

• PMO Alignment: Skilled in working within structured PMO environments, providing accurate resource estimates, timelines, and risk assessments. 
• Methodologies: Experience in both Agile (Scrum, Kanban) and Waterfall project lifecycles, adapting to the client’s governance framework. 
• Stakeholder Engagement: Demonstrated ability to translate technical complexities into actionable insights for diverse stakeholders—IT teams, faculty, administration, and executive leadership. 

5. Data Governance & Quality

• Data Governance Frameworks: Capable of establishing standards, policies, and definitions for consistent data usage across multiple campuses and systems. 
• Quality Assurance: Uses automated testing, data profiling, and validation scripts to maintain data accuracy and reliability throughout the pipeline. 
• Metadata Management: Experience implementing tools and processes for cataloging data assets, tracking lineage, and managing data dictionaries. 

6. Soft Skills & Additional Strengths 

• Team Leadership & Mentoring: Mentors junior team members, conducts training sessions, and fosters a collaborative environment aimed at knowledge sharing. Continuous Innovation: Stays current with emerging data technologies (e.g., machine learning for student success analytics, event-driven architectures, serverless computing). 
• Communication & Reporting: Provides regular project status updates, dashboards, and executive summaries to ensure transparency and accountability. 

All your information will be kept confidential according to EEO guidelines.