Our client is designing and delivering a modern enterprise data platform for the Wealth Management domain. The platform will consolidate data from multiple CRM, portfolio management, trading, reporting, risk, and compliance systems into a governed, trusted, and scalable source of truth.
The platform will support analytical, operational, regulatory, and client-facing use cases.
This is an architecture role with a primary focus on platform architecture, integration design, Data Vault architecture, governance, and delivery guidance. The person will define the target-state architecture, integration patterns, data modeling approach, and implementation standards, working closely with engineering teams responsible for delivery.
A key focus will be the architecture and design of a Data Vault-based platform on Databricks, enabling scalable ingestion, historization, auditability, lineage, and downstream consumption.
Requirements:
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Strong experience designing Databricks Lakehouse architectures, including Spark, SQL, Delta Lake, Unity Catalog, and workflow orchestration concepts.
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Proven experience architecting enterprise data platforms, preferably in financial services or Wealth Management environments.
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Strong practical understanding of Data Vault 2.0 architecture.
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Experience defining integration architecture for enterprise data landscapes with multiple source systems and consumption channels.
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Experience designing governance and security models (Unity Catalog, RBAC/ABAC, row/column-level security, PII protection, and compliance).
Nice to have:
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Experience in Wealth Management, Private Banking, Asset Management, Investment Management, Capital Markets, or broader financial services.
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Experience with Databricks Lakeflow Connect, Lakeflow Spark Declarative Pipelines, and Lakeflow Jobs.
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Experience designing platforms that support regulatory, audit, compliance, risk, and financial reporting requirements.
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Experience with MDM concepts.
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Experience with cloud infrastructure on AWS, Azure, or GCP.
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Experience with AI/RAG, semantic search, Vector Search, embeddings, metadata filtering, source-grounded generation, or LLM-based data enrichment is a plus, but not a core requirement.
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dbt experience.
Responsibilities:
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Own the target-state architecture for a Databricks-based data platform.
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Define platform architecture, integration patterns, data flows, architectural principles, and delivery guardrails for the engineering teams.
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Design scalable data ingestion, transformation, orchestration, and publication patterns using Databricks, Spark, SQL, Delta Lake, Unity Catalog, and Lakeflow where appropriate.
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Lead the Data Vault architecture, including raw vault, business vault, hubs, links, satellites, historization patterns, PIT tables, bridge tables, and downstream consumption layers.
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Define canonical data models for core Wealth Management entities such as clients, households, advisors, accounts, portfolios, holdings, transactions, products, instruments, mandates, risk profiles, fees, suitability, and regulatory classifications.
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Design integration patterns across heterogeneous source systems, including CRM, portfolio management platforms, trading systems, custodians, market data providers, risk systems, reporting platforms, and compliance tools.
We offer:
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Projects for such clients as PayPal, Wargaming, Xerox, Philips, Adidas and Toyota.
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Competitive compensation that depends on your qualification and skills.
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Career development system with clear skill qualifications.
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Flexible working hours aligned to your schedule.
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Options to work remotely.
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