Senior Data Engineer

Asset Mark•Charlotte, NC

37d•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 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 is a strategic blend of deep technical architecture and 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. We can consider candidates for this position who are able to accommodate a hybrid work schedule and are close to our Charlotte, NC office.

Requirements

Technical Depth: Expert proficiency in Python and Advanced SQL. Deep, hands-on experience with Snowflake (architecture, performance tuning, Snowpark) and Microsoft Azure data services.
Leadership & Design: Proven experience leading technical design sessions, defining target state architectures, and mentoring senior engineers.
DataOps Fluency: Strong experience with modern data stack tools, including dbt (Data Build Tool) and workflow orchestration (Airflow, Azure Data Factory).
Domain: Experience working with large-scale, complex datasets, preferably within the Financial Services or Asset Management industry.
Soft Skills: Exceptional communication skills with the ability to articulate complex technical trade-offs to non-technical executive stakeholders.
7+ years of progressive experience in Data Engineering or Software Engineering, with a significant portion dedicated to cloud data platforms.
Candidates must be legally authorized to work in the US to be considered. We are unable to provide visa sponsorship for this position.

Responsibilities

Platform Leadership: Define, champion, and drive the technical vision for our modern data architecture on Azure and Snowflake. This includes making key decisions on Lakehouse patterns, data modeling methodologies (Dimensional, Data Vault), and the strategic use of services like Azure Synapse and Azure Data Factory.
End-to-End Design: Lead the architectural design and implementation of highly scalable and resilient ELT/ETL pipelines, ensuring optimal performance for mission-critical financial workloads.
Build vs. Buy: Provide expert technical guidance and contribute to the evaluation and selection of new data tools and frameworks (e.g., orchestration, observability, vector databases).
Cost Optimization: Drive FinOps practices within the data platform, focusing on optimizing Snowflake compute usage, storage costs on Azure, and overall cost-per-query efficiency.
Hands-on Coding & Delivery: Serve as a hands-on technical leader by writing, optimizing, and reviewing complex code primarily in Python and SQL. Directly contribute to the most challenging parts of data pipeline development.
Standards & Governance: Define, document, and enforce engineering best practices, architectural design patterns, and coding standards across the data team.
Code Review / PR Process Ownership: Oversee the code review process, providing constructive, high-quality technical feedback to ensure that all committed code is scalable, secure, maintainable, and aligns with the defined vision.
Mentorship: Actively mentor and coach junior and mid-level data engineers on technical depth, debugging complex distributed systems, and modern data stack methodologies.
CI/CD & DevOps: Lead the integration of data solutions into CI/CD pipelines (e.g., Azure DevOps, GitHub Actions), ensuring robust testing, deployment automation, and operational readiness.
Data Quality & Observability: Own the strategy and implementation of Data Observability solutions (like Monte Carlo) to proactively monitor the health, freshness, volume, and lineage of all production datasets.
Data Lineage & Cataloging: Ensure comprehensive data lineage is captured and maintained to support transparency, auditing, and impact analysis across the platform.
Security & Compliance: Collaborate closely with security and compliance teams to design and implement rigorous data governance policies, including PII masking, data tokenization, and Role-Based Access Control (RBAC) specific to financial data.
SLA Management: Define, monitor, and enforce data Service Level Agreements (SLAs) and Service Level Objectives (SLOs) for critical data assets, and lead blameless post-mortems following any data incident.
AI Data Strategy: Partner with Data Science and Product teams to architect the necessary data flows and infrastructure to support AI/ML model training, inference, and MLOps.
GenAI Integration: Provide technical leadership in piloting and implementing Generative AI (GenAI) techniques-leveraging LLMs via tools like Snowflake Cortex or open-source frameworks-to automate engineering tasks (code generation, documentation) and enable new data products.
Feature Engineering: Guide the team on best practices for designing and curating versioned, high-quality feature sets for production-ready machine learning models.