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AssetMark

Third-party asset management platform for advisors

Data Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeCharlotte, NC, USA

About the Job

Requirements

  • Have 3–7 years of professional experience in Data Engineering, Analytics Engineering, or a similar role, with demonstrable progression in scope and complexity.
  • Have expertise with Snowflake architecture, optimization, and advanced SQL features.
  • Have strong proficiency in SQL and dbt for data transformation; Python experience is a plus.
  • Have solid experience with data modeling techniques, including dimensional modeling, and developing complex ETL/ELT workflows.
  • Be able to learn business domains, earn trust with business users and data stewards, and drive clarity from ambiguity to deliver well-defined data solutions from requirement through delivery.
  • Have hands-on experience building and deploying data solutions in a cloud environment; Azure is preferred.
  • Have experience with modern data transformation tools such as dbt and orchestration tools such as Azure Data Factory or Airflow.

Responsibilities

  • Design, build, and optimize scalable, fault-tolerant ELT/ETL pipelines using dbt and SQL to transform complex datasets in Snowflake and deliver enterprise-scale data assets for analytics and user consumption.
  • Own the Azure data infrastructure, including Azure Data Factory and Azure Synapse, and manage data flows into Snowflake.
  • Lead the design and implementation of dimensional, Kimball, or Inmon models in the data warehouse for analytics and reporting.
  • Tune complex SQL queries and Snowflake data pipelines for low latency and cost-efficient compute usage.
  • Champion software engineering best practices, including unit and integration testing, automated data validation, and resilient CI/CD pipelines using tools such as Azure DevOps or GitHub Actions.
  • Define and enforce data quality standards across the transformation layer, designing dbt and other automated tests to validate accuracy, completeness, and consistency at each data architecture layer.
  • Implement data quality frameworks and observability solutions such as Monte Carlo to monitor data freshness, volume, distribution, and schema health and prevent data downtime.
  • Establish and maintain data lineage documentation and tooling such as Atlan for transparency and compliance across the data transformation layer.
  • Ensure data assets and pipelines adhere to financial-industry compliance, governance, and security standards, including role-based access control, encryption, and personally identifiable information masking.
  • Evaluate and pilot generative AI techniques, including large language models through GitHub Copilot or open-source frameworks, to accelerate development, generate boilerplate code, and enhance documentation.
  • Mentor junior data engineers on SQL, dbt, Snowflake, data modeling, and cloud architecture best practices.
  • Partner with users and data stewards to learn domain context, elicit and clarify business rules, evaluate tradeoffs, and translate consumption needs into well-designed, scalable data assets.

Desired Qualifications

  • Experience working in financial services or wealth management.
  • Prior exposure to data observability platforms such as Monte Carlo or Atlan.
  • Familiarity with generative AI concepts or hands-on use of large language model coding assistants such as Copilot to improve engineering efficiency.
  • Experience with real-time or streaming data technologies such as Kafka or Azure Event Hubs.
  • Exposure to Infrastructure as Code tools such as Terraform.
  • Python experience.

About the Company

AssetMark

Third-party asset management platform for advisors

AssetMark provides a comprehensive, third-party asset management platform that empowers independent financial advisors by offering investment solutions, advanced technology, and consulting services. Its products work as an integrated suite: a platform with investment options, technology tools for client onboarding and portfolio management, and advisory consulting that acts as an extension of the advisor’s team. This setup helps advisors deliver personalized guidance and run their practices more efficiently, with revenue generated from platform and service fees. Compared to competitors, AssetMark combines a broad investment lineup, advisor-centric support, and seamless technology integration to streamline wealth management rather than relying on standalone products. The company's goal is to help financial advisors grow their practices and better serve clients with tailored, efficient solutions.

Company size
1,001-5,000
Company stage
IPO
Headquarters
California, United States
Founded
1996

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