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 Job/What You'll Do:
The Senior Data Engineer / Technical Lead is a pivotal, hands-on leadership role responsible for the end-to-end design, governance, and operational excellence of AssetMark's data platform. This role blends deep technical architecture with team enablement, serving as the bridge between business needs and production-grade data systems. The focus is on driving highly scalable solutions and pioneering the integration of AI/ML models into our data ecosystem.
This role will help define the technical direction of a modern data platform while remaining close to delivery. The successful candidate will guide architecture, write and review complex code, establish engineering standards, mentor data engineers, and partner with Data Science, Product, Security, Compliance, and business stakeholders to deliver reliable, governed, and AI-ready data products.
We can only consider candidates for this position who are able to accommodate a hybrid work schedule and are close to our Charlotte, NC office.
Key Responsibilities
Define, champion, and drive the technical vision for AssetMark's modern data architecture on Azure and Snowflake, including strategic use of Snowflake, dbt, Fivetran, Azure Data Lake, Azure Synapse, and Azure Data Factory.
Lead the end-to-end architectural design and implementation of scalable, resilient ELT/ETL pipelines that support mission-critical financial workloads and remain reliable as data volume, complexity, and business demand grow.
Serve as a hands-on technical leader by writing, optimizing, and reviewing complex Python and SQL code. Directly contribute to the most challenging aspects of data pipeline development and help the team solve difficult distributed-systems problems.
Define and enforce engineering best practices, architectural design patterns, coding standards, testing practices, and documentation standards across the data team. Own a constructive code review and pull-request process that promotes scalable, secure, maintainable solutions.
Lead the integration of data solutions into CI/CD and DevOps processes using tools such as Azure DevOps and GitHub Actions, ensuring automated testing, repeatable deployments, and operational readiness.
Own DataOps strategy and reliability, including data quality, observability, freshness, volume, lineage, cataloging, SLAs, SLOs, incident response, and blameless post-mortems for critical data assets.
Partner with Security and Compliance to implement data governance policies for financial data, including PII masking, data tokenization, Role-Based Access Control, lineage, auditing, and appropriate access management.
Drive FinOps practices within the data platform by optimizing Snowflake compute, Azure storage, and overall cost-per-query efficiency. Provide technical guidance for build-versus-buy decisions involving orchestration, observability, vector databases, and other emerging data technologies.
Partner with Data Science and Product teams to architect data flows and infrastructure for AI/ML model training, inference, and MLOps. Provide technical leadership for GenAI initiatives using Snowflake Cortex or open-source frameworks, and guide the design of versioned, high-quality feature sets.
Mentor junior and mid-level data engineers, lead technical design sessions, articulate complex trade-offs to executive stakeholders, and create structure and momentum in a fast-paced, evolving data environment.
Knowledge, Skills & Abilities
Expert proficiency in Python and advanced SQL, with deep hands-on experience designing, developing, optimizing, and troubleshooting production data pipelines.
Deep experience with Snowflake architecture, performance tuning, Snowpark,…