Senior Data Engineer

AssetMark Financial Holdings•Charlotte, NC

9d•Hybrid

About The Position

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.

Requirements

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, compute and storage optimization, and the design of scalable cloud data platforms on Microsoft Azure.
Strong knowledge of modern data engineering tools and patterns, including dbt, Fivetran, Azure Data Factory, Azure Synapse, Azure Data Lake, workflow orchestration, ELT/ETL, and CI/CD automation.
Strong understanding of data modeling and architecture methodologies, including Lakehouse patterns, dimensional modeling, Data Vault, data lineage, data cataloging, and platform integration patterns.
Hands-on DataOps and reliability experience, including data observability, quality monitoring, testing, freshness and volume monitoring, SLAs/SLOs, incident response, post-mortems, and operational governance.
Knowledge of data security and compliance practices, including PII masking, tokenization, Role-Based Access Control, auditing, and governance requirements for financial data.
Working knowledge of AI/ML data architecture, model training and inference flows, MLOps, feature engineering, Generative AI, LLM-enabled data products, Snowflake Cortex, and related open-source frameworks.
Exceptional communication and collaboration skills, with the ability to influence technical direction, explain trade-offs to non-technical stakeholders, mentor engineers, and lead through ambiguity.
10 or more years of progressive experience in Data Engineering or Software Engineering, with a significant portion focused on cloud data platforms.
Proven experience leading technical design sessions, defining target-state architectures, establishing engineering standards, and mentoring senior engineers while remaining hands-on.
Experience designing and operating production-grade data systems that support large-scale, complex datasets and business-critical workloads.
Experience working within Financial Services or Asset Management is required, with an understanding of the reliability, governance, security, and audit expectations associated with financial data.
Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field preferred; equivalent practical experience may be considered.
Relevant certifications in Azure, Snowflake, data engineering, cloud architecture, or related disciplines are a plus.

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.