Director, Data Engineering (Analytics Engineering)

Job Description:

AssetMark is a leading strategic provider of innovative investment and consulting solutions serving independent financial advisors. We provide investment, relationship, and practice management solutions that advisors use in helping clients achieve wealth, independence, and purpose.

The Director of Analytics Engineering & Business Intelligence is a pivotal leadership role responsible for the design, governance, and delivery of the enterprise's consumption-ready data models and analytical products. This role leads the function that transforms raw, integrated data into standardized, trustworthy assets used for reporting, business intelligence (BI), and strategic decision-making. The Director must possess strong technical expertise in modern analytics tools and proven leadership in aligning data solutions with critical business outcomes.

We can consider candidates for this position who are able to accommodate a hybrid work schedule and are close to our Charlotte, NC office.

I. Strategy, Governance, and Vision

• Analytics Strategy: Define and execute the strategic roadmap for the Analytics Engineering function, ensuring the delivery of a canonical metric layer and scalable BI solutions that align directly with AssetMark's commercial and product goals.

• Metrics Governance: Establish and enforce enterprise standards for all core business metrics and Key Performance Indicators (KPIs). Own the source of truth for financial and operational definitions, ensuring consistency across all dashboards and reports.

• Semantic Layer Architecture: Lead the design and implementation of the semantic layer (e.g., using specialized tools or dbt-based metrics layers), enabling self-service and high-performance querying for the entire analytics community.

• Stakeholder Partnership: Serve as the strategic partner to executive leadership, Product, Finance, and Marketing teams, translating complex business questions into clear, actionable data modeling requirements.

II. Modeling & Delivery Excellence

• Dimensional Modeling: Lead the team in designing and implementing robust data models (e.g., Kimball Dimensional Models, Star Schemas) that are optimized for analytic performance and business user clarity.

• Transformation Oversight (dbt): Own the entire dbt (Data Build Tool) ecosystem and transformation layer. Set standards for model testing, documentation, and lineage capture to ensure the delivered data is reliable and auditable.

• Consumption Architecture: Architect the systems and processes that efficiently move final-stage (Gold/Presentation) data into BI tools (e.g., Power BI, Tableau) and downstream operational applications.

• Code Review & Quality: Define and enforce high-quality coding standards primarily for SQL and Python transformations. Oversee code review and validation processes to ensure the integrity of all production data models.

III. Leadership & Business Enablement

• Team Leadership & Development: Build, mentor, and lead a high-performing team of analytics engineers and BI developers. Foster a culture of excellence, data literacy, and direct business impact.

• Self-Service Enablement: Drive the strategy to democratize data by providing user-friendly tools and well-governed data marts that empower business users to answer their own questions.

• Cross-Functional Delivery: Collaborate directly with the Director of Data Engineering to ensure seamless data flow from the ingestion pipelines (Bronze/Silver) into the modeling layer (Gold/Presentation).

• External Partnership: Manage vendor relationships related to BI, data visualization, and specialized analytics tools, ensuring effective utilization and integration with the core platform.

Required Qualifications

• Experience: 10+ years of progressive experience in Data Warehousing, Business Intelligence, or Data Analytics, with at least 3+ years in a senior leadership or management role.

• Technical Expertise: Expert proficiency in Advanced SQL and Python. Deep, hands-on architectural experience with dbt (Data Build Tool) in a production environment.

• Platform: Strong experience with modern cloud data warehouses like Snowflake or Databricks.

• Modeling: Expert knowledge of Dimensional Modeling (Kimball), data mart design, and performance tuning for analytical workloads.

• Leadership: Proven ability to lead, mentor, and hire high-performing techni