Senior Data Engineer​/Data Platform; Wealth Management​/RIA

We are a rapidly growing RIA with over $8 billion in AUM focused on helping the growing number of retiring advisors with succession. We execute our strategy through a full-integration model by acquiring and scaling high-quality advisory firms throughout the US that are led by advisors looking to retire and transition their clients to Next Gen staff advisors. As we continue to scale, we are building a best-in-class finance organization to support growth, value creation, and long-term performance.

• Own the end-to-end design and delivery of Vest Gen’s modern data platform across Snowflake and Azure, including ingestion, transformation, orchestration, and serving layers that support reporting, analytics, and operational workflows.
• Build and maintain scalable data pipelines integrating core systems such as Orion (portfolio accounting/reporting, holdings, performance, billing, transactions), eMoney (planning inputs/outputs, household financial profiles), Salesforce Financial Services Cloud (FSC) (households, relationships, activities, pipeline, service), Zocks (meeting/notes extraction, structured insights, workflow triggers), and Microsoft 365 (SharePoint/Teams/Outlook sources as needed for operational data and document workflows).
• Standardize data modeling and semantic definitions for wealth‑management entities (e.g., Household, Client/Entity, Account, Registration, Security/Product, Transaction, Billing, planning artifacts), ensuring consistent definitions across Power BI and downstream consumers.
• Implement robust data governance, security, and compliance controls appropriate for an RIA environment, including role‑based access patterns, least privilege, PII handling, auditing/monitoring, data classification, retention practices, and secure data sharing patterns.
• Enable operational analytics and automation by publishing trusted datasets and APIs/exports that power Power BI dashboards and metrics, Power Platform apps/flows (Power Automate approvals, exception handling, work queues), and stakeholder reporting (executive, Ops, Investment team, Compliance).
• Drive data quality and observability: automated tests, reconciliation checks (custodian vs. Orion vs. warehouse), anomaly detection, lineage documentation, and SLAs for critical datasets.
• Lead modern delivery practices: version control, CI/CD where appropriate, environment management (dev/test/prod), and repeatable deployment patterns for Snowflake objects and Azure components.
• Partner tightly with business stakeholders (Operations, Investments, Advisors, Compliance) to translate workflows into data products—prioritizing outcomes like reduced manual work, faster onboarding/integration of acquisitions, and improved advisor/client insights.
• Advance AI‑enabled workflows responsibly by supporting structured extraction/enrichment use cases (e.g., meeting insights from Zocks, document classification, automated commentary, exception triage) while ensuring controls for accuracy, auditability, and human‑in‑the‑loop review where needed.

• 8–12+ years in data engineering, analytics engineering, or data platform roles delivering production‑grade pipelines and enterprise reporting.
• Deep expertise in SQL, dimensional/semantic modeling, and building scalable ELT/ETL pipelines (batch plus near‑real‑time where needed).
• Strong hands‑on experience with Snowflake, including performance tuning, security patterns, and building curated layers (raw → refined → curated/semantic).
• Strong hands‑on experience with Azure data and security fundamentals (identity/access patterns, secure networking concepts, storage integration patterns).
• Proficiency in Python (data processing, automation, API integrations) and scripting for pipeline operations.
• Practical experience integrating SaaS platforms via APIs, file drops, event/webhook patterns, or managed connectors—especially CRM and financial platforms.
• Proven ability to implement data quality, reconciliation, monitoring, and incident response for mission‑critical pipelines.
• Strong communication skills: able to translate ambiguity into clear deliverables, document data products, and align stakeholders on…