Senior Data Engineer

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.