Senior Data Analyst

Job Description: AssetMark is a leading wealth management platform dedicated to empowering independent financial advisors. AssetMark's mission is to enable financial advisors to make a profound difference in the lives of their clients. Over 10,000 advisors partner with AssetMark for our investment offerings, innovative technology, advanced services, and expertise, which they use to delight their clients and grow their businesses. We are an integrated team of technologists, investment professionals, and operations experts working to help our clients stay at the pioneering front of wealth management.

The Job/What You'll Do: The Senior Data Analyst leads requirements gathering and translates business needs into scalable analytics and data solutions. This role designs complex dashboards and self-service analytics while serving as a bridge between Analytics and Analytics Engineering during AssetMark's transition to Snowflake. The Senior Data Analyst partners with Analytics Engineering to develop and validate canonical models and gold-layer curated datasets, leads migration of existing analytical solutions from legacy data sources, and builds team capability through standards, mentoring, training, and reusable development patterns. The role also identifies and responsibly implements AI-assisted tools and automation to improve development efficiency, documentation, testing, data quality, and support. This role manages delivery and ensures solutions are accurate, adopted, and drive business impact.

Key Responsibilities Lead requirements gathering and translate business needs into technical specifications for analytics solutions and data products. Design complex dashboards, semantic models, and scalable self-service analytics that meet user needs and functional requirements. Partner with Analytics Engineering to define and develop canonical models and gold-layer curated datasets in Snowflake, including SQL and dbt transformations, tests, documentation, and data quality controls. Identify and responsibly implement approved AI-assisted tools and automation to improve efficiency in SQL and dbt development, documentation, testing, migration, data quality, and user support while maintaining human review, security, and data governance. Lead migration of dashboards, semantic models, and other analytical assets to curated Snowflake views through source mapping, reconciliation, validation, and controlled retirement of legacy dependencies. Evaluate new BI and analytics engineering capabilities, optimize performance, and establish best practices for scalable architecture, data modeling, query design, testing, documentation, and dashboard UI/UX. Provide technical mentorship and strengthen the Analytics community of practice through training, code reviews, reusable patterns, and standards for Snowflake, dbt, analytics engineering, and responsible AI-assisted workflows. Manage solution development and migration projects by coordinating business stakeholders, analysts, data engineers, and architects to ensure timely delivery. Drive adoption and measure data quality, solution usage, and business impact to inform future development priorities.

Knowledge, Skills & Abilities: Advanced proficiency in SQL, Snowflake, and dbt, with strong knowledge of data engineering and analytics engineering practices, including ELT, dimensional modeling, testing, lineage, and data quality. Proven experience designing, launching, and maintaining advanced analytical solutions and self-service BI products using Power BI or comparable platforms. Experience migrating analytical workloads from legacy platforms to curated cloud data models using source-to-target mapping, reconciliation, Git-based version control, and code review practices. Demonstrated experience applying AI-assisted analytics or development tools to improve efficiency, quality, or scalability, with sound judgment around output validation, data privacy, security, and responsible use. Strong partnering, communication, stakeholder management, and technical mentoring skills across business, Analytics, and Data Engineering teams.

Education & Experience: 5+ years in analytics, BI, analytics engineering, or related data roles, with experience leading requirements gathering, solution development, and data migration projects. Bachelor's or Master's degree in Analytics, Statistics, Computer Science, Business, or a related field; advanced degree preferred.

Why Join AssetMark? Help shape AssetMark's transition from legacy data platforms to Snowflake and trusted, reusable data products. Work hands-on at the intersection of analytics and analytics engineering, contributing to canonical models and gold-layer curated datasets. Build lasting team capability through mentorship, shared standards, modern development practices, and responsible AI-enabled ways of working. Apply AI and automation in practical ways that make analytical products more efficient to develop, document, test, and support.