Associate Data Engineer

AssetMark•Charlotte, NC

4d•$94,000 - $110,000•Hybrid

About The Position

AssetMark is a leading wealth management platform dedicated to empowering independent financial advisors. AssetMark's mission is to enable financial advisors to make a profound difference in the lives of their clients. Over 10,000 advisors partner with AssetMark for our investment offerings, innovative technology, advanced services, and expertise, which they use to delight their clients and grow their businesses. We are an integrated team of technologists, investment professionals, and operations experts working to help our clients stay at the pioneering front of wealth management. We are seeking a talented Associate Data Engineer, Analytics Engineering, to join our data engineering team in Charlotte. This position offers an exceptional opportunity to accelerate your career in a collaborative environment where you’ll gain hands-on experience building trusted data pipelines, transformations, and analytic data assets for the wealth management industry. As an Associate Data Engineer, you’ll work alongside experienced data and analytics engineers who will mentor you while you contribute to meaningful projects that drive our business forward. You’ll help transform raw data into governed, high-quality datasets using modern tools such as SQL, dbt, Snowflake, Git, and cloud data platforms. We’ve designed a clear pathway for your advancement as you develop technical expertise, business domain knowledge, and the ability to take on increasingly complex responsibilities. 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

Solid foundation in SQL with demonstrated ability to analyze, transform, and validate data
Exposure to or interest in dbt, Snowflake, and modern analytics engineering practices
Experience with version control systems, especially Git, and collaborative workflows including branches and pull requests
Familiarity with Python for scripting, automation, or data manipulation a plus
Basic understanding of data modeling, ETL/ELT concepts, data quality, and analytics/reporting workflows
Strong problem-solving skills with ability to break down complex or ambiguous problems methodically
Excellent communication skills with ability to discuss technical concepts clearly to technical and non-technical audiences
Comfort working with business users to understand processes, clarify requirements, and translate business needs into data requirements
Demonstrated commitment to continuous learning and professional growth
Familiarity with cloud platforms, especially Azure, a plus
Exposure to data governance, catalog, lineage, or observability tools such as Atlan or Monte Carlo a plus
Interest in financial technology or wealth management domains a plus
0-3 years of experience in data engineering, analytics engineering, business intelligence, data analysis, software engineering, or a related technical role; recent graduates with relevant project work or internships are encouraged to apply
Bachelor's degree in Computer Science, Data Analytics, Information Systems, Engineering, Mathematics, Statistics, Business Analytics, or related technical field, or equivalent practical experience

Responsibilities

Build, test, and maintain dbt/SQL data transformations and pipelines that deliver reliable, analytics-ready datasets in Snowflake
Assist in developing dimensional models, curated data marts, and reusable data assets that support reporting, analytics, and business decision-making
Implement data validations, tests, and clear documentation to improve data accuracy, completeness, consistency, lineage, and trust
Partner with analysts, data stewards, engineers, and business users to understand processes, clarify data requirements, and translate business needs into scalable data solutions
Apply analytical thinking to investigate data or tool issues, validate assumptions, resolve technical challenges, and propose practical solutions with appropriate guidance
Use Git-based development workflows, including branching, pull requests, code reviews, testing, and team standards for maintainable data code
Responsibly leverage and build AI-assisted methods to accelerate SQL/dbt development, documentation, testing, and troubleshooting
Actively participate in agile ceremonies, retrospectives, knowledge-sharing sessions, and structured mentorship to develop professional and technical skills
Learn modern data governance and observability concepts, including lineage, ownership, freshness, schema health, and data reliability