Senior data architect, financial accounting and cloud data platforms

• Design and own a canonical analytics data model that normalizes inputs from multiple accounting source systems into a unified, system-agnostic schema for downstream consumption
• Define and document a layered data architecture with clear boundaries and interface contracts between:
  • source-aligned data models
  • a canonical hub layer
  • semantic or application-facing models
• Manage the end-to-end model lifecycle across multiple releases, including schema evolution, field deprecation, controlled extensions, and backward compatibility for downstream consumers
• Establish and enforce a versioning approach for the canonical model and interface specifications so teams consistently reference a clearly versioned, authoritative design
• Create a repeatable source onboarding pattern that allows additional accounting platforms to be integrated incrementally without requiring model redesign
• Define a Snowflake implementation approach across layers, including decisions on virtualization versus materialization and appropriate use of dynamic tables, Snowpark, and materialized views
• Design semantic models for business intelligence consumption, including star schemas with fact tables and conformed dimensions (such as security, account, date, geography, and sector)
• Translate risk engine input requirements into a standardized data feed model that maps canonical positions, prices, security master data, and over-the-counter terms and conditions into risk inputs
• Define a Snowflake storage model for risk outputs, including fact structures for value at risk results, stress scenarios, and factor exposures, with scenario identifiers and version metadata
• Align performance-related data models to shared canonical structures to reduce duplication and clarify ownership of shared schema elements
• Define ingestion patterns for third-party performance data using shared adapters when appropriate
• Produce engineering-ready specifications including model definitions, transformation rules, implementation requirements, and acceptance criteria
• Document designs in Confluence in a format aligned to engineering intake processes and support architecture alignment workshops with solution architects and delivery teams

Required experience and skills

• Significant hands-on experience with institutional investment accounting platforms
• Deep understanding of accounting book of record and investment book of record concepts, including differences in timing, completeness, position sourcing, and downstream usage
• Experience modeling complex instruments and structures, including multi-leg derivatives, fund-of-fund look-through, private market holdings, and over-the-counter contracts
• Proven ability to normalize and reconcile structural and semantic differences across multiple accounting sources into a unified canonical schema
• Production experience designing layered data architectures in Snowflake, including:
  • materialization strategy using dynamic tables, Snowpark, materialized views, and virtual layers
  • semantic layer and star schema design for business intelligence tools such as Power BI or Tableau
  • compute and cost-aware architecture, including query optimization, clustering, partitioning strategies, and billing-aware design
  • Snowpark usage for in-platform computation using Python or Scala
• 10 or more years of data architecture experience, emphasizing enterprise and canonical data modeling in financial services
• Demonstrated ability to design source-agnostic models that accommodate multiple upstream systems without repeated schema redesign
• Strong capability producing formal design artifacts, including entity-relationship diagrams, field-level specifications, transformation rules, and data classifications
• Strong grounding in normalization principles, key design, audit attributes, and lineage patterns
• Experience managing model evolution across multiple delivery phases, including versioning, deprecation paths, controlled breaking changes, and downstream impact assessment
• Domain experience in institutional financial data, such as positions, transactions, securities, benchmarks, performance, and risk
• Experience authoring and maintaining architecture decision records as a living governance mechanism
• Ability to communicate complex architecture as precise, build-ready specifications for engineering teams
• Experience documenting in Confluence and working with Jira-style backlog and intake processes
• Strong facilitation skills for cross-functional architecture alignment sessions in distributed teams

FAQ

1. What are the core responsibilities of a Senior Data Architect in this role?
This role focuses on designing and governing data architectures that support financial accounting processes on cloud platforms. It includes defining data models, integration patterns, and ensuring data consistency across systems. The architect also aligns data solutions with regulatory, reporting, and business requirements.

2. How does financial accounting influence data architecture in this position?
Financial accounting requires high accuracy, traceability, and compliance in data design. The architect ensures that data models support general ledger, reporting, and audit requirements. Strong controls and lineage tracking are essential for financial data integrity.

3. What cloud platforms and technologies are commonly used?
Common platforms include AWS, Azure, and Google Cloud for building scalable data ecosystems. Technologies such as data warehouses, data lakes, and ETL/ELT pipelines are widely used. Tools like Snowflake, BigQuery, or Azure Synapse may be part of the architecture.

4. What role does data modeling play in this job?
Data modeling is a critical responsibility, including designing conceptual, logical, and physical data models. The architect ensures models support financial reporting, analytics, and operational use cases. Proper modeling improves performance, scalability, and maintainability.

5. How is data governance handled in this role?
The architect establishes data governance frameworks, including data quality standards, access controls, and lineage tracking. This ensures compliance with financial regulations and internal policies. Governance helps maintain trust and reliability in financial data.

6. How does this role collaborate with finance and technology teams?
The architect works closely with finance stakeholders to understand accounting requirements and translate them into data solutions. Collaboration with engineering teams ensures proper implementation of architectures. Clear communication bridges business and technical perspectives.