We turn institutional data into a highly effective asset by combining powerful technology platforms with best practices from the field. Our solutions present data in ways that allow higher education stakeholders to take informed actions and mitigate risk. We couple unmatched institutional expertise with proven data models, insightful workflows, and innovative software

How You will Help:

You will be responsible for leading and managing the data engineering team to build, maintain, and optimize data infrastructure and systems. Your role will involve overseeing the design, development, and deployment of data pipelines, databases, and data integration solutions, ensuring high-quality data is available, accessible, and ready for analysis. You will collaborate closely with cross-functional teams, such as data science, analytics, and software engineering, to support data-driven decision-making across the organization. Your strategic vision, technical expertise, and leadership skills will play a critical role in driving the success of the data engineering function and its impact on the company's growth and innovation.

What You will Do:

• Team Leadership: Lead, mentor, and manage a team of data engineers, fostering a collaborative and high-performance culture. Provide technical guidance, set goals, conduct performance evaluations, and ensure the team's professional growth.
• Data Architecture and Strategy: Develop and execute a robust data engineering strategy aligned with the company's goals. Design scalable and efficient data architecture to support current and future data needs, including data warehousing, data lakes, and data integration processes.
• Data Pipeline Development: Oversee the design, implementation, and maintenance of data pipelines to efficiently extract, transform, and load (ETL) data from various sources into the data ecosystem. Ensure data quality and integrity throughout the process.
• Database Management: Manage databases and data storage solutions, ensuring optimal performance, security, and reliability. Choose appropriate database technologies and implement data partitioning, indexing, and optimization techniques.
• Technology Evaluation and Integration: Stay up to date with emerging data engineering technologies, tools, and frameworks. Evaluate their potential for integration into the existing data infrastructure to improve efficiency and scalability.
• Collaboration with Data Science and Analytics: Collaborate with data science and analytics teams to understand their data requirements and provide them with reliable and accessible data for analysis and modeling purposes.
• Data Governance and Compliance: Establish and enforce data governance policies and standards to maintain data privacy, security, and compliance with relevant regulations (e.g., GDPR, CCPA).
• Performance Monitoring and Optimization: Implement monitoring and alerting systems to proactively identify and resolve performance bottlenecks and data-related issues.
• Project Management: Plan and prioritize data engineering projects, ensuring timely and successful execution within budget constraints. Communicate project status and key metrics to stakeholders.
• Cross-Functional Collaboration: Collaborate with other teams, including software engineering, product management, and business stakeholders, to align data engineering initiatives with broader business objectives.

You will Be a Good Fit for this Role if You Have:

• Bachelor's or Master’s degree in Computer Science, Engineering, or a related field.
• Proven experience (typically 8+ years) in data engineering, including hands-on experience with data pipeline development, data warehousing, and ETL processes.

• Strong leadership and managerial experience, with a track record of building and leading successful data engineering teams.
• Proficiency in programming languages such as Python, Java, Scala, or similar.
• In-depth knowledge of data storage systems (e.g., relational databases, NoSQL databases, data lakes) and big data technologies (e.g., Hadoop, Spark).

Nice To Have

• Familiarity with cloud-based data platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
• Solid understanding of data governance, data security, and data compliance principles.
• Excellent problem-solving skills and the ability to analyze complex data engineering issues.
• Strong communication skills, with the ability to convey technical concepts to non-technical stakeholders.