Principal Data Engineer

Our vision for the future is based on the idea that transforming financial lives starts by giving our people the freedom to transform their own. We have a flexible work environment, and fluid career paths. We not only encourage but celebrate internal mobility. We also recognize the importance of purpose, well-being, and work-life balance. Within Empower and our communities, we work hard to create a welcoming and inclusive environment, and our associates dedicate thousands of hours to volunteering for causes that matter most to them.

Chart your own path and grow your career while helping more customers achieve financial freedom. Empower Yourself. Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time, including CPT/OPT.

The Principal Engineer, Data will provide technical leadership across the design, development, modernization, and operation of enterprise data platforms and analytics solutions. This role will define scalable data engineering patterns, lead complex data integration and transformation initiatives, and enable reliable, governed, and high-performing data products that support business reporting, analytics, and advanced AI/ML use cases. The Principal Engineer, Data will also serve as a senior technical expert and mentor, helping modernize legacy reporting and ETL platforms into cloud-native, automated, and analytics-ready data architecture.

Provide technical feasibility analysis and solution evaluation for data and analytics initiatives based on business, operational, and regulatory needs. Lead the design and implementation of scalable data engineering solutions, including data pipelines, data models, data integrations, and analytics-ready data products. Provide technical leadership for data architecture, ETL and ELT design, code reviews, performance tuning, production support, and issue resolution. Guide the modernization of legacy data and reporting platforms, including migration from traditional ETL and reporting tools to modern cloud data platforms and transformation frameworks.

Design and oversee data pipelines using cloud-based technologies, Python, Spark, SQL, and related orchestration and transformation tools. Support enterprise analytics platforms, including data warehouse, reporting, semantic layer, and business intelligence environments. Partner with data analysts, data engineers, business stakeholders, architects, product owners, and technology partners to translate business needs into resilient data solutions. Define and execute the technical roadmap for data engineering, analytics enablement, platform modernization, data quality, and automation.

Establish and promote engineering standards, reusable patterns, coding guidelines, testing practices, data quality controls, and documentation expectations. Collaborate with architecture, security, infrastructure, governance, and business teams to ensure data solutions are secure, reliable, scalable, and aligned with enterprise standards. Mentor engineers and technical team members, supporting skill development in cloud data engineering, data modeling, analytics engineering, and modern data platform practices.

Support AI/ML enablement by ensuring data pipelines, curated datasets, and feature-ready data assets are reliable, governed, and suitable for advanced analytics and machine learning use cases. Stay informed about emerging trends in data engineering, cloud, analytics, AI/ML, and automation to support continuous improvement and technical innovation.

Bachelor's degree in Computer Science, Information Systems, Data Engineering, Analytics, Engineering, or equivalent training and experience. 12 or more years of experience in software engineering, data engineering, analytics engineering, or enterprise data platform development. Proven experience designing, developing, and maintaining complex data solutions involving multiple systems, stakeholders, business domains, and production dependencies. Strong experience with Python, SQL, distributed data processing, and ETL and ELT development.

Experience with cloud-based data platforms and services, including development, deployment, monitoring, and operational support of data solutions. Strong understanding of data modeling, data warehousing, data quality, metadata management, data lineage, performance optimization, and production support practices. Working knowledge of AI/ML concepts and the data engineering practices required to support advanced analytics, machine learning, and model-ready datasets. Proven ability to design data solutions that integrate with internal and external systems while meeting security, governance, scalability, and reliability requirements.

Deep understanding of software development and data engineering practices in a distributed team environment, including version control, testing, CI/CD, release management, and operational monitoring.…