Senior Data Architect - Snowflake

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.***

As a Senior Data Architect at Empower, you will be both a strategic leader and hands on technical expert shaping our enterprise data analytics platform and data product architecture. Partnering with enterprise architects, business leaders, and IT teams, you will define solution architectures, establish scalable platform patterns, and guide teams toward secure, high performing, cost efficient data capabilities. You will influence key architectural decisions, mentor teams, and help advance technology excellence through practical standards, reusable templates, and strong engineering outcomes. 

What you will do:

• Own end to end solution architecture for connecting source systems to the enterprise analytics platform and delivering governed, performant, reusable data products for analytics and machine learning use cases 

• Define reference architectures and implementation patterns for Snowflake based solutions, including storage integration and stages, networking connectivity, key management, secure sharing, replication and disaster recovery approaches, and workload and warehouse strategy 

• Establish security on the platform including role-based access models, masking approaches, row level controls, and auditable access and usage practices, partnering with the data governance team for classification and policy inputs 

• Define ingestion and integration blueprints for batch, change data capture, and event driven pipelines, including guidance for when to use streaming patterns for low latency data movement 

• Set standards for contract first data products including semantics, versioning rules, reliability expectations, and clear consumption patterns for BI, analytics, and ML use cases, partnering with governance teams on catalog and stewardship processes 

• Build performance and reliability playbooks including workload isolation, concurrency patterns, sizing strategies, cost guardrails, and operational runbooks teams can execute 

• Establish and promote architectural standards, best practices, and review mechanisms while mentoring and coaching technology team members to ensure consistent adoption 

• Collaborate with business and engineering teams to design end to end solutions that meet strategic, technical, and operational objectives and can be delivered incrementally 

• Evaluate enabling tools and approaches for pipelines, quality, observability, and automation, and build business cases that influence direction 

• Guide engineering teams through solution design and implementation, improving long term scalability, performance, data quality, and cost efficiency 

What you will bring:

• Strong, hands-on Snowflake experience, with the ability to define practical platform patterns and guide teams through implementation and operations 

• Working knowledge of Snowflake capabilities such as warehouses and workload management, cost controls, secure sharing, Tasks and Streams, and platform security controls 

• Deep experience delivering enterprise analytics and data platform solutions, typically gained through about 5 to 12 years in a data specialty such as engineering, platforms, or analytics, with 2+ years leading end to end solution design for complex initiatives 

• Strong understanding of modern data platform architecture including ingestion, transformation, modeling, consumption patterns, and operational readiness for analytics and ML workloads 

• Strong SQL capability including performance tuning fundamentals, plus proficiency in Python or a comparable language used for data engineering and automation 

• Practical experience addressing cross-cutting concerns including security, privacy, resiliency, scalability, observability, and cost optimization in cloud data platforms 

• Proven abilit