Lead Engineer Data

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.

The Lead Data Engineer provides hands-on technical leadership in the design, development, and optimization of scalable data pipelines, ingestion frameworks, and data platforms. This role leads complex initiatives, ensures alignment with architectural standards, and drives delivery of high-quality, secure, reliable and properly governed data solutions and data platforms aligned with enterprise standards. Operating with significant independence, the Lead Data Engineer guides technical decision-making, mentors engineers, and ensures consistent implementation of best practices across teams. Serves as an escalation point for complex data engineering challenges.

The team currently uses SAS Viya and Amazon Redshift for data processing, automation, and reporting and is transitioning toward Snowflake. This role combines direct coding, troubleshooting, and support of existing workflows with participation in Snowflake engineering and design alongside enterprise engineering teams. Production ownership and new development remain core responsibilities as the future environment evolves.

•    Lead the design and implementation of advanced data pipelines and ingestion frameworks. Personally develop, test, and maintain the underlying SQL, Python, and ETL code.

•    Guide technical execution of complex data initiatives across teams. Balance current production commitments with incremental modernization; assess which processes to retain, migrate, redesign, or retire.

•    Support and troubleshoot SAS Viya and SQL-based production workflows. Trace job failures and data discrepancies to root cause, implement safe corrections, validate outputs, and improve monitoring and recovery.

•    Translate highly complex business requirements into scalable data solutions. Work with subject-matter experts to clarify business definitions and reconcile outputs into consistent, reusable datasets.

•    Ensure data platforms are optimized for performance, scalability, and reliability. Consider Snowflake query performance and compute cost.

•    Enforce data governance, security, and quality standards.

•    Mentor engineers and provide technical leadership on projects. Share knowledge, document processes, and build backup ownership across the team.

•    Lead code reviews and ensure adherence to engineering standards. Use version control, automated testing, and CI/CD practices to support safe releases.

•    Collaborate with architects and stakeholders to align solutions with enterprise direction. Communicate technical decisions and production issues clearly to technical and nontechnical partners in a matrixed environment.

•    Drive improvements in automation, efficiency, and platform capabilities.

•    Support architectural discussions and contribute to design decisions. Help design Snowflake transformations, dimensional models, and shared analytics-ready data structures.

•    Validate migrations through reconciliation, parallel runs, and rollback planning before retiring established workflows.

•    Complete other duties as assigned.

•    Bachelor’s degree in Computer Science or related field, or equivalent combination of training and experience.

•    6–8+ years of data engineering experience with enterprise scale platforms.

•    Advanced proficiency in SQL, including query optimization and complex transformations, and strong hands-on Python skills.

•    Strong experience with AWS and modern data platforms. Hands-on Snowflake engineering experience is required.

•    Deep understanding of data modeling, ETL/ELT processes, and data warehousing. Experience with dimensional modeling and reusable business-facing datasets.

•    SAS/SAS Viya experience is strongly preferred. Candidates with strong SQL, procedural programming, and enterprise ETL experience must be willing and able to learn and support SAS-based workflows.

•    Demonstrated hands-on production support, persistence in resolving issues, and experience making safe changes to unfamiliar or complex existing code.

•    Experience leading technical initiatives and mentoring engineers.

•    Strong problem-solving and communication skills. Ability to explain complex technical matters clearly, listen to business users, and collaborate constructively across teams.

•    Experience working in Agile environments.

•    PySpark experience is preferred. Experience preparing governed data for advanced analytics or machine learning is a plus.

This job operates in a professional office environment.

This job description is not intended to be an exhaustive list of all duties, responsibilities and qualifications of the job. The employer has the right to revise this job description at any time. You will be evaluated in part based on your performance of the responsibilities and/or tasks listed in this job description. You may be required to perform other duties that are not included on this job description. The job description is not a contract for employment, and either you or the employer may terminate employment at any time, for any reason, as per terms and conditions of your employment contract.

***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.***