Head of Enterprise Data

Location

Remote (U.S.), with approximately 25% travel — including monthly onsite time in Newport Beach, CA or Dallas, TX and periodic travel to affiliate offices during integration work.

Job Description

Beacon Pointe is one of the largest independent RIAs in the country, and we are growing quickly – both organically and through the acquisition of advisory firms across the U.S. That growth has made enterprise data a strategic priority: every acquisition brings new systems, new data, and new definitions that must be reconciled into a single trusted view of our clients, accounts, advisors, and business performance.

We are seeking an experienced enterprise data-platform leader to own the strategy, architecture, governance, and operational maturity of our Snowflake-centered data ecosystem. Reporting to the Chief Technology Officer, this leader will build and develop the data engineering and analytics organization, establish trusted enterprise data products and common business definitions, and ensure that data is secure, resilient, traceable, cost effective, and readily usable across advisory, operations, finance, compliance, and technology.

This is a role for someone equally comfortable leading people, discussing business requirements with senior stakeholders, and going deep with engineers on Snowflake architecture and data-modeling decisions. You will be accountable not only for platform delivery, but for data quality, production reliability, governance, business adoption, and the data foundation supporting analytics, automation, and AI.

Key Responsibilities

Team Leadership & Management

• Le ad, mentor, and develop a team of data engineers and data analysts , establishing clear goals, priorities, and accountability.
• Manage the team's roadmap and balance strategic initiatives, business requests, platform improvements, and ongoing operational needs.
• Establish engineering and analytics standards around development, testing, documentation, deployment, and data quality.
• Foster strong collaboration between engineering, analytics, technology, operations, finance, investment, and other business teams.
• Recruit and develop data talent as the organization and data function continue to scale.

Acquisition & Data Integration

• Own a repeatable, documented playbook for onboarding acquired firms’ data – discovery, mapping, migration, reconciliation, validation, and legacy system decommissioning.
• Map acquired-firm client, household, account, and advisor records to enterprise entities, resolving duplicates and conflicting identifiers.
• Migrate and reconcile historical holdings, transactions, performance, and billing data so that continuity of client reporting is preserved through the transition.
• Partner with M&A team, operations, and compliance during diligence to assess data condition and integration effort before deals close.
• Optimize time-to-integration and cost-per-integration as deal volume grows, so that integration capacity is not a constraint on firm growth.

Data Platform & Engineering

• Own and evolve the organization's Snowflake data environment , ensuring it is scalable, reliable, secure, performant, and cost-effective.
• Provide technical leadership for data ingestion, transformation, integration, and delivery across multiple internal and external data sources.
• Guide the development of robust ETL/ELT pipelines and modern data engineering practices.
• Establish standards for monitoring, testing, lineage, observability, and troubleshooting across the data platform.
• Own platform-level design decisions across environment and deployment strategy, RBAC and access controls, data classification and policy-based protection, secure data sharing, account governance, recovery and resilience, and integration with identity, security, and enterprise monitoring platforms.
• Partner with technology and security teams to establish appropriate data access, governance, and security controls.

Data Modeling & Architecture

• Lead the design and implementation of enterprise data models that provide consistent definitions and trusted data across the organization.
• Develop and maintain dimensional, relational, and analytical data models that support reporting, analytics, and downstream applications.
• Establish reusable data layers and common business entities to reduce duplication and improve consistency across reporting and analytics.
• Translate complex business processes into scalable data structures and clearly defined metrics.
• Ensure data models are well documented, maintainable, performant, and designed to evolve with the business.

AI on the data platform

• Builds the governed, well-modeled, documented, and quality-controlled data foundation that makes enterprise data AI-ready.
• Applies AI within the platform and the engineering practice itself
• Leverages platform-native AI capabilities

Analytics & Business Partnership

• Partner with business leaders to understand reporting, analytics, and data needs and translate them into technical solutions.
• Oversee the development of dashboards, reporting datasets, analytical models, and self-service data capabilities.
• Establish consistent definitions for key business metrics and promote a trusted single source of truth for enterprise reporting.
• Help stakeholders move beyond static reporting toward actionable insights and data-driven decision-making.
• Communicate technical concepts, tradeoffs, and recommendations clearly to both technical and non-technical audiences.

Program & delivery execution

• Plans and delivers multi-workstream data programs, platform build-out, source integrations, and analytics
• Manages roadmap, scope, dependencies, and stakeholders across engineering, technology, and the business
• Sequences work to balance strategic initiatives against operational demand.
• Brings a repeatable delivery model for onboarding acquired firms' data (custodial feeds, account/household master, performance, billing) so integrations land on time and to a consistent standard rather than as one-off scrambles.

Data Governance & Quality

• Establish and maintain standards for data quality, governance, lineage, documentation, and ownership .
• Implement processes for identifying, prioritizing, and resolving data quality issues.
• Partner with business stakeholders to define authoritative data sources and ownership for critical data domains.
• Support appropriate controls around sensitive financial, client, and personally identifiable information.
• Ensure the data platform supports the firm’s regulatory obligations as an SEC-registered investment adviser.

Qualifications

Required

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, Analytics, or a related field, or equivalent professional experience.
• 10+ years of experience in data engineering, data architecture, analytics engineering, business intelligence, or related data roles.
• Demonstrated experience leading a data-engineering or enterprise-data function supporting a complex, regulated organization, including responsibility for platform strategy, architecture, delivery, production operations, talent, and stakeholder management.
• Hands-on depth in Snowflake, including multi-workload architecture, security and RBAC design, performance and cost optimization, and environment and deployment strategy.
• Demonstrated experience designing and implementing data models, including dimensional modeling, star/snowflake schemas, and analytical data marts.
• Strong SQL skills and a deep understanding of relational databases and modern cloud data platforms.
• Experience designing and supporting scalable ETL/ELT pipelines and integrating data from multiple source systems.
• Experience establishing data quality, testing, documentation, and governance practices.
• Ability to translate business requirements into scalable technical solutions.
• Strong prioritization, communication, and stakeholder-management skills, with the ability to translate business requirements into scalable technical solutions.
• Experience in Financial Services (wealth, asset management, banking, insurance, or capital markets).

Preferred

• Experience in RIA.
• Understanding of RIA and wealth-management data, including clients and households, accounts, custodians, holdings, transactions, performance, billing, CRM, and financial advisor data.
• Experience integrating data from custodians, portfolio accounting systems, CRMs, financial planning platforms, or other wealth-management technology.
• Additional Snowflake depth in data classification and policy-based controls, secure data sharing, account governance, observability and access auditing, recovery and resilience, and integration with identity and enterprise monitoring platforms.
• Familiarity with tools such as dbt, Python, orchestration platforms, BI/visualization tools, and modern data catalog or governance solutions.
• Experience implementing semantic layers, governed metrics, or self-service analytics environments.
• Experience in a growing organization where data platforms and processes are being standardized and scaled.

What Success Looks Like

This leader is expected to have delivered against the following outcomes:

• A functioning team and operating model with filled roles, published standards, defined support model and satisfactory business engagement.
• Reliable data platform with defined SLAs on business-critical pipelines being met.
• Repeatable integration playbook with acquisition integration measurably faster.
• Transparent Snowflake and tooling cost and business usage metrics.

About Beacon Pointe Advisors

Beacon Pointe Advisors is one of the nation’s largest Registered Investment Advisor firms with headquarters in Southern California and affiliate offices nationwide. Beacon Pointe provides advisory services to a range of clients, including institutions (i.e., endowments, foundations), high-net-worth individuals, and families. Beacon Pointe has been recognized by various industry publications including Forbes, Financial Advisor Magazine, Barron’s, and more. For more information, please visit Awards Disclosures .