Director, Data Engineering

AssetMark•Charlotte, NC

1d•$192,000 - $240,000•Hybrid

About The Position

AssetMark is a leading wealth management platform dedicated to empowering independent financial advisors. Our mission is to enable financial advisors to make a profound difference in the lives of their clients. More than 10,000 advisors rely on AssetMark for our investment offerings, innovative technology, and expert services. We are an integrated team of technologists, investment professionals, and operations experts working to keep our clients at the forefront of wealth management. As AssetMark continues to invest in enterprise data capabilities, we are building a modern data platform that improves how data is ingested, governed, transformed, consumed, and activated across the business. This role will be a critical leadership position in Charlotte, helping shape the platform foundation and guide migration from legacy data patterns into a Snowflake-centered operating model. We are looking for a leader who can turn platform strategy into execution. This role is accountable for building the engineering muscle behind our enterprise data platform: establishing strong architectural patterns, enabling domain teams, reducing legacy complexity, and ensuring that analytics, product, operations, and AI/ML use cases are supported by governed, reliable, well-modeled data. 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

10+ years of progressive experience in Data Engineering, Data Architecture, Software Architecture, or Technology Leadership, with at least 3+ years in people management or dedicated technical leadership.
Direct experience leading modern cloud data platform initiatives using Snowflake, including performance tuning, environment strategy, RBAC/security, data sharing, workload management, and cost optimization.
Hands-on experience with Fivetran or comparable managed ingestion platforms, including connector governance, schema drift handling, ingestion monitoring, and source-to-target validation.
Deep practical experience with dbt, including model design, macros, tests, documentation, exposures, lineage, CI/CD, and promotion patterns across environments.
Strong SQL and Python skills and the ability to review technical designs and code with credibility; experience with orchestration, data observability, version control, and CI/CD practices.
Proven success leading data platform migrations, modernization programs, or large cross-functional data initiatives with dependency management, cutover planning, reconciliation, and stakeholder communication.
Strong understanding of data governance, data quality, metadata, privacy, PII controls, auditability, and regulatory expectations in a financial-services environment.
Demonstrated ability to set engineering standards, drive architectural consensus, and balance speed, quality, cost, security, and long-term maintainability.
Candidates must be legally authorized to work in the US to be considered.
We are unable to provide visa sponsorship for this position.

Nice To Haves

Financial Services, Wealth Management, or FinTech experience, including familiarity with custodial data, account/household data, trade processing, billing, performance, and regulatory reporting.
Experience establishing medallion-style data layers, data product operating models, semantic/serving layers, or governed self-service analytics patterns.
Experience enabling downstream Data Science, AI/ML, product analytics, and operational reporting teams through curated data products and reliable feature-ready datasets.
Experience managing vendor contracts, platform spend, and enterprise adoption of cloud data technologies.

Responsibilities

Build the platform foundation: Lead engineering design and delivery for the new data platform, including ingestion standards, Snowflake environment strategy, role-based access control, data zones/layers, metadata management, and service-level expectations.
Create the enterprise ELT operating model: Drive adoption of Fivetran for managed ingestion, Snowflake for scalable storage and processing, and dbt for governed transformations, data quality tests, documentation, and reusable data models.
Enable trusted data products: Partner with data consumers to deliver curated, reusable data assets that support analytics, reporting, product experiences, client/advisor insights, operations, and future AI/ML workloads.
Operationalize engineering quality: Embed automated testing, CI/CD, observability, cost monitoring, lineage, data contracts, and runbooks into platform delivery from the start rather than treating them as after-the-fact controls.
Own the migration roadmap: Lead planning and execution for migrating priority data domains, pipelines, models, and reporting dependencies from legacy platforms and bespoke integrations into the target Snowflake/Fivetran/dbt architecture.
Manage cutover and coexistence: Define phased migration strategies, dependency maps, rollback plans, data validation routines, parallel-run approaches, and business-readiness checkpoints to minimize operational risk.
Drive reconciliation and trust: Ensure migrated datasets meet clear acceptance criteria for completeness, accuracy, timeliness, lineage, access control, and auditability before decommissioning legacy assets.
Coordinate cross-functional delivery: Orchestrate migration activities across Data Engineering, Application Engineering, Analytics, Data Governance, Security, Infrastructure, Product, and business owners; manage milestones, risks, issues, and executive communication.
Stay technically engaged: Remain hands-on in architecture reviews, critical code reviews, data model reviews, and production-readiness decisions across Python, SQL, Snowflake, dbt, and orchestration patterns.
Run the platform like a product: Own platform reliability, performance, SLAs/SLOs, incident response, capacity planning, Snowflake cost optimization, and continuous improvement of developer experience.
Translate governance into controls: Implement data quality, lineage, classification, privacy, PII handling, audit trails, and access-control policies through automated engineering practices and platform guardrails.
Support AI/ML readiness: Architect pipelines and curated feature-ready datasets that enable data science experimentation, model training, inference, and responsible AI governance.
Lead and scale the team: Build, mentor, and develop a high-performing team of data engineers and architects, creating a culture of ownership, craftsmanship, technical rigor, and measurable delivery.
Partner at the executive level: Communicate platform strategy, tradeoffs, migration progress, risks, and investment needs in a way that builds confidence with senior technology and business leaders.
Manage strategic partners: Own relationships with key platform vendors and data providers, including Snowflake, Fivetran, dbt, cloud providers, custodians, and other data ecosystem partners.
Champion enterprise adoption: Help teams move from siloed data delivery to reusable, governed data products, creating clear intake, prioritization, support, and enablement models.