Data Engineering Manager

Data Engineer Manager
Ascent is a leading provider of innovative financial products and student support services that enable more students to access education and achieve academic and economic success. Everything Ascent offers is designed with the best-in-class teams and technology to increase every student's ability to plan, pay, and succeed in their education and career. Ascent's planning tools and financing resources are co-created by students for those in college, graduate school, and career training programs. Ascent offers financial wellness education, student success services, and one-on-one and digital career coaching that put students' academic and professional goals within reach. Ascent has been widely recognized for its strong culture – named “Best Places to Work” by the San Diego Business Journal for the past 5 years – and as an industry leader – named “Best Student Loan” by NerdWallet for the past 3 years. 
Description
We are seeking a dynamic and experienced Data Engineering Manager who will oversee a high-performing engineering team, drive innovative machine learning projects, and ensure robust, scalable data solutions. This role is ideal for an experienced data professional who thrives in a hands-on technical environment. This leader will not only refine our development processes but also enhance our ETL pipelines, implement advanced AWS technologies, and mentor team members to excel in delivering next-generation ML, AI, and data-driven applications. In addition, your work will democratize data access and allow all teams to leverage data-driven insights.
Responsibilities

• Team Leadership & Mentorship: Lead, mentor, and manage a team of data and machine learning engineers, fostering a collaborative, growth-oriented environment focused on technical excellence and continuous learning.
• Project Management & Execution: Oversee end-to-end execution of machine learning and data pipeline projects, from research and prototyping to production deployment, ongoing optimization, and integration with business goals.
• Data Pipeline Optimization: Design, refactor, and optimize legacy data pipelines, ensuring scalability, efficiency, and accuracy. Leverage modern tools (e.g., AWS Glue, dbt, SSIS) to streamline and enhance ETL/ELT workflows.
• Architectural Strategy: Drive architectural decisions and technical direction, utilizing AWS services (e.g., SageMaker, Lambda, Redshift, EC2) and best practices for cloud-based data processing and machine learning workflows.
• Cross-Functional Collaboration: Work closely with product, data science, operations, and business teams to align technical solutions with data needs and strategic objectives, ensuring measurable value is delivered.
• Machine Learning Integration: Support the integration of machine learning models into data pipelines, including applications like custom credit risk scoring systems.
• Code Quality & Best Practices: Establish and uphold best practices for code quality, testing, CI/CD pipelines, infrastructure as code, and security, ensuring high reliability, performance, and data governance.
• System Monitoring & Reliability: Monitor system performance, address issues proactively, and ensure data security, encryption, and compliance with governance standards.
• Innovation & Continuous Improvement: Foster a culture of innovation, staying ahead of industry trends to integrate cutting-edge tools and methodologies that drive actionable insights and advanced analytics.

Qualifications

• 10+ years of software or data engineering experience, with at least 3 years in a leadership or management role.
• Proven leadership abilities, including experience managing teams, driving cross-functional initiatives, and coordinating between technical teams and business units.
• Strong communication and collaboration skills, with the ability to convey complex technical concepts to both technical and non-technical audiences.
• Strong expertise in machine learning frameworks, tools, and workflows, with a proven track record of delivering ML projects into production.
• In-depth knowledge of AWS technologies, including SageMaker, Redshift, Glue, and other data services, with hands-on experience in cloud-based data solutions.
• Extensive experience in ETL pipeline design and deployment strategies, particularly with SSIS, dbt, and modern data stack tools.
• Strong programming skills in SQL and Python, with the ability to design and implement robust data solutions.
• Deep understanding of modern data architectures, distributed systems, and scalable infrastructure.
• Experience with Agile methodologies and CI/CD pipelines to ensure reliable, repeatable, and rapid deployments.
• Experience with data lake architectures and event-driven data processing.
• Familiarity with data visualization tools such as Tableau, AWS QuickSight, or similar platforms.
• Solid understanding of database technologies, including relational (e.g., MS SQL, Postgres) and NoSQL (e.g., DynamoDB) (preferred).
• Basic knowledge of machine learning applications in areas such as finance, credit risk assessment, and fraud detection.

Education

• Bachelor’s degree in Computer Science, Engineering, or related technical field. Advanced degrees are a plus.
• Certifications in AWS or Machine Learning are a plus.