Head of Data Architecture

LifeStance Health

1d•$190,000 - $237,000•Remote

About The Position

The Director, Data Architecture owns the enterprise data model and the architecture standards that govern how data is structured, conformed and validated across the enterprise data platform. This player-coach leader defines and approves canonical models, Bronze and Silver design standards and conformance requirements, and holds architectural approval authority over enterprise data designs. The role directs a team of Technical Data Analysts, Data Quality Analysts and MDM Data Stewards who translate analytical use cases into model designs, validate that implemented data products conform to the approved design, and steward master data resolution. The role owns the logical and physical design of the enterprise data model; Data Engineering builds the pipelines that populate it and operates it in production. Architectural decisions made here govern how every enterprise data product is structured, secured and consumed, and carry direct consequence for HIPAA compliance, regulatory reporting accuracy and analytical trust. Technical Data Analysts are embedded as durable members of domain-oriented data-product teams alongside Data Engineers and Enterprise Analytics partners. The director leads the Data Architecture professional practice and ensures enterprise consistency while domain teams share one backlog, delivery plan and definition of done for high-quality silver data products. Consistent with LifeStance’s values, every member of the LifeStance team is expected to support each other and the mission, which may mean participating in projects and initiatives and performing functions and responsibilities not specifically outlined in this job description.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Management, Analytics or a related field, or equivalent relevant experience.
10+ years of progressive experience in data architecture, enterprise data modeling, data warehousing, technical data analysis or enterprise information management.
10+ years of healthcare data experience.
5+ years of experience leading and managing data architecture, data governance or data management teams, including performance management, capability development and accountability for a function.
Deep expertise in conceptual, logical, and physical data modeling, canonical and conformed data structures, data grain, history and cross-domain integration, and in setting enterprise model standards for naming, documentation, metadata and design review. Dimensional design of gold-layer analytical products remains with Enterprise Analytics.
Demonstrated ability to translate analytical questions and anticipated consumption patterns into reusable enterprise data-model designs without owning business metric definitions.
Deep experience with source-system discovery, data profiling, source-to-target mapping, transformation requirements, reconciliation and implementation acceptance criteria.
Experience partnering with Data Engineering teams responsible for the physical implementation and production operation of cloud data-warehouse pipelines that populate models owned by Data Architecture.
Strong working knowledge of modern cloud data platforms such as Snowflake, Redshift or Databricks and transformation frameworks such as dbt, without requiring platform-administration ownership.
Experience with healthcare data domains and the modeling considerations associated with clinical, patient, provider, encounters/visits, payer, financial or operational data.
Working knowledge of data governance, metadata, lineage, data quality and master/reference-data practices, including the distinction between technical architecture and business governance ownership.
Demonstrated ability to lead through influence across Data Engineering, Enterprise Analytics, business functions, Platform Engineering and executive stakeholders.
Strong facilitation and communication skills, including the ability to make complex model decisions understandable to both technical and analytical audiences.
Qualified candidates must be legally authorized to be employed in the United States
Demonstrates awareness, inclusivity, sensitivity, humility, and experience in working with individuals from diverse ethnic backgrounds, socioeconomic statuses, sexual orientations, gender identities, and other various aspects of culture.

Nice To Haves

Experience establishing enterprise data architecture during a migration from Redshift to Snowflake or another major platform modernization.
Experience organizing data delivery around durable business domains or data products.
Experience leading an architecture or modeling chapter whose practitioners are embedded in cross-functional delivery teams.
Experience in a regulated healthcare environment supporting HIPAA, HITRUST or comparable requirements.
Experience with identity resolution, master/reference-data architecture, semantic layers, interoperability standards or AI-ready data foundations.
Comfort in operating hands-on during initial function build-out, including direct modeling work alongside the team while the practice is established.

Responsibilities

Define, own and approve the enterprise data-architecture strategy, standards, decision framework and roadmap in alignment with data-platform, analytics, interoperability and AI priorities.
Hold architectural approval authority over enterprise data designs. New sources, models and integration patterns may not proceed to production without conformance to the standards this role establishes and approves.
Lead the development of enterprise conceptual, logical, and physical models covering core clinical, patient, provider, encounter/visits, payer, financial, operational and workforce domains.
Establish architectural standards across Bronze and Silver layers, including source-aligned schema models, schema organization, naming conventions, change-data-capture patterns, audit metadata standards, retention policies and PHI handling requirements. Bronze is a designed and governed layer, not an ungoverned raw landing zone.
Own the data quality and validation function for enterprise data products, directing Data Quality Analysts who define and execute validation rules confirming that implemented data is correct — no duplicate patients, every encounter tied to a valid provider, required fields populated, referential integrity intact and record counts reconciled to source.
Direct MDM Data Stewards responsible for resolving records flagged by the master data management platform, adjudicating identity matches and tracing global identifier history when the business identifies discrepancies.
Model conformed silver-layer entities, including grain, business keys, relationships, cardinality, history management, effective dating, canonical structures and cross-source reconciliation.
Lead, mentor and develop Technical Data Analysts and enterprise data modelers as a professional chapter, strengthening analytical discovery, source profiling, logical modeling, specification and validation capabilities.
Embed Technical Data Analysts as durable members of domain data-product teams, ensuring they participate from use-case discovery through implementation validation and consumer acceptance rather than functioning as an upstream requirements queue.
Partner with Enterprise Analytics and functional analytics teams to understand intended gold-layer products, analytical questions and metric needs without assuming ownership of business metric definitions, reports, dashboards or analytical interpretation.
Translate analytical use cases into implementable silver-layer designs, including entities, grain, relationships, historical requirements, source-to-target mappings, conformance rules, transformation requirements and acceptance criteria.
Partner with Data Engineers during source discovery and design to ensure logical models are technically feasible, source-aware and informed by physical implementation, performance and operational considerations.
Validate that implemented silver-layer products conform to the approved logical design and can support the intended analytical use cases before consumer acceptance.
Establish model documentation, metadata, lineage, traceability, reconciliation and known-use limitation standards for enterprise data products.
Approve all changes to enterprise data models. Material changes to shared entities, grain, keys or cross-domain structures require formal architecture review; changes that conform to published standards follow an expedited approval path so governance does not become a delivery bottleneck.
Define the technical architecture for master and reference data, including identifiers, matching capabilities and integration patterns, in partnership with business data owners and the future Data Governance function.
Establish interim definition, stewardship and model-decision mechanisms with Enterprise Analytics and business owners until the enterprise Data Governance function is established, then transition formal governance accountability while retaining technical architecture ownership.
Partner with Data Platform Engineering on Snowflake, dbt, CI/CD, environments, observability, access-control implementation and reusable technical frameworks required to implement and populate data models consistently.
Measure and improve model reuse, analytical fitness, design-cycle time, implementation rework, architectural consistency and consumer satisfaction with silver-layer products.