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.
Role Overview
As a Lead Data Engineer, you will play a critical role across the full data lifecycle—from business requirements gathering and data modeling to solution architecture, development, deployment, and production support. This is a hands-on technical leadership role requiring deep expertise in building scalable, cloud-native data platforms using Snowflake, Amazon Redshift, AWS, and modern data engineering tools.
You will lead the design and implementation of high-performance data pipelines, real-time streaming architectures, and analytics-ready data models while driving innovation through GenAI-enabled data solutions, observability platforms, and modern transformation frameworks such as dbt. The ideal candidate is passionate about solving complex data challenges, mentoring engineering teams, and enabling enterprise-scale analytics and AI initiatives.
What You Will Do
Design, develop, and implement scalable batch and real-time data pipelines using Snowflake, Amazon Redshift, and AWS-native technologies
Lead end-to-end data engineering initiatives including data ingestion, transformation (ETL/ELT), data quality, and data delivery across enterprise platforms
Build and optimize cloud-native data solutions leveraging AWS services such as S3, Lambda, Glue, ECS, EMR, IAM, CloudWatch, and related services
Develop and maintain modern ELT transformation frameworks using dbt (Data Build Tool) for modular, testable, and scalable data modeling
Implement and support real-time and CDC-based streaming architectures using tools such as Kafka, STRIIM, and event-driven integration frameworks
Design scalable semantic and vector-based data architectures using Vector Databases to support AI/ML and Retrieval-Augmented Generation (RAG) use cases
Collaborate with data architects, analysts, product owners, and business stakeholders to gather requirements and translate them into technical solutions
Drive best practices in data modeling, governance, metadata management, lineage, and performance optimization
Implement observability and monitoring solutions using tools such as Datadog, CloudWatch, and custom alerting frameworks to ensure platform reliability and operational excellence
Lead code reviews, establish engineering standards, and champion CI/CD and DevOps practices for data platforms
Troubleshoot and resolve complex production issues related to data pipelines, orchestration, and warehouse performance
Mentor junior and mid-level engineers, fostering a culture of technical excellence and continuous learning
Evaluate and adopt emerging technologies in cloud, AI/ML, and data engineering to drive innovation across the organization
Enable AI/ML initiatives by building trusted, scalable, and high-quality datasets for advanced analytics and GenAI applications
Partner with cross-functional teams to implement secure, compliant, and highly available enterprise data solutions
What You Will Bring
Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field
12+ years of experience in Data Engineering, Data Warehousing, and Software Development
Strong expertise in SQL and Python with experience building enterprise-scale data solutions
Hands-on experience with Snowflake and Amazon Redshift in large-scale production environments
Strong experience with modern data transformation frameworks such as dbt
Experience with orchestration and workflow tools such as Apache Airflow
Hands-on experience with streaming and CDC technologies such as Kafka, STRIIM, or similar event-streaming platforms
Experience building and supporting observability frameworks using Datadog or equivalent monitoring platforms
Familiarity with Vector Databases and AI/GenAI integration patterns for intelligent data applications
Exposure to GenAI tools, LLM-powered workflows, or AI-enabled analytics platforms is highly preferred
Strong understanding of dimensional and normalized data modeling techniques
Experience working with AWS cloud services including S3, Lambda, Glue, IAM, ECS, CloudFormation, and related technologies
Knowledge of CI/CD implementation using tools such as GitHub Actions, Jenkins, Terraform, or similar platforms
Experience with data governance, data quality frameworks, and security best practices
Excellent problem-solving, analytical, and communication skills
Proven ability to lead technical initiatives and mentor engineering teams
Passion for innovation, continuous learning, and adopting modern data engineering practices
We are an equal opportunity employer with a commitment to diversity. All individuals, regardless of personal characteristics, are encouraged to apply. All qualified applicants will receive consideration for employment without regard to age, race, color, national origin, ancestry, sex, sexual orientation, gender, gender identity, gender expression, marital status, pregnancy, religion, physical or mental disability, military or veteran status, genetic information, or any other status protected by applicable state or local law.