Job Title: Data Architect — Aggregated Analytical Warehouse (FNZ)
About FNZ:
FNZ is a global fintech firm transforming the way financial institutions serve their clients. By
combining cutting-edge technology, infrastructure, and investment operations, FNZ
enables wealth management firms to deliver personalized investment solutions at scale.
Operating across multiple regions and supporting over $1.5 trillion in assets under
administration, FNZ partners with leading banks, insurers, and asset managers to create
seamless and innovative wealth platforms that empower millions of investors worldwide.
Job Summary:
We are seeking a Data Architect to design and own the architecture of the Aggregated
Analytical Warehouse — FNZ's cross-client analytics platform that delivers industry-level
benchmarks, risk signals, and aggregated insights with mathematically guaranteed privacy.
This role architects the three-layer privacy stack (federated architecture, confidential
compute, differential privacy) and defines how aggregated analytics are modelled,
computed, and served. This is one of the most architecturally novel roles on the platform,
as no competitor in wealth management offers cross-client analytics with this level of
privacy assurance.
Key Responsibilities:
• Aggregated Warehouse Architecture: Design the end-to-end architecture for the
Aggregated Analytical Warehouse — federated data collection, confidential
compute processing, differential privacy output, and aggregated data
storage/serving on Microsoft Fabric.
• Three-Layer Privacy Stack Design: Architect the privacy stack:
• Layer 1 — Federated Architecture: Design how each client ODS computes local
aggregates with raw data never leaving client boundaries.
• Layer 2 — Confidential Compute: Architect the confidential clean room using
Azure Confidential Clean Rooms or Opaque Systems (AMD SEV-SNP enclaves)
where aggregations run with hardware attestation.
• Layer 3 — Differential Privacy: Design the SmartNoise/OpenDP integration that
applies calibrated noise to all outputs, with formal epsilon-differential privacy
guarantees and privacy budget management.
• Cross-Client Data Model: Define the aggregated data models for cross-client
analytics — AUM distribution benchmarks, asset allocation mix by segment, fee
structure comparisons, portfolio concentration indices, sector exposure trends,
trade volume patterns, and rebalancing activity signals.
• Flink Analytics Architecture: Architect the Apache Flink deployment downstream
of Gold topics for real-time cross-client aggregations — windowed rollups (5-
min/hourly/daily), complex event processing (unusual trading activity patterns), and
streaming joins across entity types.
• Federated ML Architecture: Design the architecture for federated ML models using
federated learning frameworks — local training on each client ODS, gradient
aggregation in confidential enclaves, differential privacy on gradient updates to
prevent gradient inversion attacks. Define use cases: cross-client anomaly
detection, industry-wide risk signal models, shared compliance patterns.
• Feature Store Architecture: Architect the Feature Store (Hopsworks/Feast)
integration — how Flink-computed features (sliding window calculations) are served
for both batch training and real-time inference at sub-millisecond latency.
• Confidential Compute Vendor Evaluation: Lead the technical evaluation of Azure
Confidential Clean Rooms vs. Opaque Systems. Define evaluation criteria, run pilot
architectures for both, and make a vendor recommendation based on maturity,
financial services fit, and long-term strategic alignment.
• Privacy Budget Management: Design the privacy budget framework — how epsilon
budgets are allocated across query types, time windows, and clients. Ensure that
cumulative privacy loss remains within acceptable bounds over time.
• Regulatory Architecture: Ensure the aggregated analytics architecture supports
GDPR right-to-erasure, DORA ICT risk monitoring, and BCBS 239 risk data
aggregation reporting. Design consent management integration for cross-client data
participation.
• Standards & Governance: Establish data modelling standards, privacy review
processes, and architecture review gates for all Aggregated Analytical Warehouse
development.
Qualifications:
• Education: Bachelor's or Master's degree in Computer Science, Engineering,
Mathematics, Statistics, or a related technical field. Advanced degree preferred.
• Experience: 8+ years of experience in data architecture or data engineering, with at
least 3 years in a data architect role. Experience with multi-party or crossinstitutional data architectures.
• Privacy Technologies: Strong understanding of differential privacy (epsilon-DP),
confidential computing (AMD SEV-SNP, Intel SGX), and federated learning/analytics
concepts. Ability to design systems that provide formal privacy guarantees.
• Microsoft Fabric / Azure: Deep experience with Microsoft Fabric, Azure Synapse, or
equivalent cloud analytical platforms. Understanding of OneLake, Delta Lake, and
data sharing protocols.
• Apache Flink: Experience architecting Flink-based streaming analytics —
windowed aggregations, stateful processing, Flink SQL, and deployment on
Kubernetes.
• Data Modelling: Expert-level skills in designing aggregated data models, statistical
summaries, benchmark indices, and time-series analytics.
• Security Architecture: Understanding of hardware-attested enclaves,
cryptographic audit trails, and zero-trust data processing architectures.
• Data Governance: Experience with data governance in multi-tenant or multiinstitutional environments — access controls, consent management, and
regulatory compliance frameworks.
Preferred Qualifications:
• Experience working in the Wealth Management or Financial Services industry with
understanding of cross-institution benchmarking, risk aggregation, or industry
analytics.
• Hands-on experience with Opaque Systems, Azure Confidential Clean Rooms, or
similar confidential compute platforms.
• Experience with SmartNoise, OpenDP, or other differential privacy frameworks in
production systems.
• Familiarity with federated learning frameworks (e.g., PySyft, FATE) and their
application in financial services (e.g., Mastercard cross-institution fraud detection,
Consilient cross-bank AML).
• Experience with Apache Kafka architecture — topic design, schema registries, and
streaming-to-analytical bridge patterns.
• Knowledge of Feature Store architectures (Hopsworks, Feast) and ML serving
infrastructure.
• Relevant certifications (Azure, Flink, privacy engineering) are a plus.
About FNZ
FNZ is committed to opening up wealth so that everyone, everywhere can invest in their future on their terms. We know the foundation to do that already exists in the wealth management industry, but complexity holds firms back.
We created wealth’s growth platform to help. We provide a global, end-to-end wealth management platform that integrates modern technology with business and investment operations. All in a regulated financial institution.
We partner with the world’s leading financial institutions, with over US$2.4 trillion in assets on platform (AoP).
Together with our clients, we empower nearly 30 million people across all wealth segments to invest in their future.
