Job Title: Platform Engineering Lead (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 an experienced Platform Engineering Lead to drive the engineering delivery

of FNZ's data platform. This role leads engineering efforts across the full platform scope —

the Near Real-Time Operational Data Store (NRT-ODS), Analytical Warehouse, AI/ML

capabilities, and Platform Security. The ideal candidate will own the technical delivery that

evolves the platform from a data delivery engine into an industry-leading insight platform,

leading engineering teams across roadmap pillars including Data Trust & Governance,

Client Data Delivery, Lakehouse & Fabric Integration, Stream Processing, Intelligence & AI,

Cross-Client Analytics, and Operational Excellence.

Key Responsibilities:

• Engineering Leadership: Lead engineering delivery across the entire data platform

— NRT-ODS streaming platform, Analytical Warehouse (Microsoft Fabric), AI/ML

layer, and platform security. Drive execution, remove blockers, and ensure

engineering quality across all pillars of the platform roadmap.

• ODS Engineering Delivery: Own the engineering delivery of the streaming-first,

event-driven platform comprising 179 Kafka Streams topologies, Debezium CDC

pipelines, 200+ Avro schemas (Apicurio Registry), OAuth 2.0 security (Key Cloak),

and Kubernetes-based deployment. Drive performance tuning, reliability

improvements, and feature delivery.

• Analytical Warehouse Delivery: Lead the engineering build-out of the Analytical

Warehouse on Microsoft Fabric, including Kafka-to-Fabric Direct Sink,

Delta/Parquet storage on One Lake, semantic layer, and future Apache Iceberg

adoption for time-travel queries and multi-engine access.

• AI & Intelligence Delivery: Drive the engineering delivery of AI capabilities including

Feature Store (Hopsworks/Feast), RAG over ODS documentation and schemas,

NL2SQL for Gold data, and domain-specific ML models. Ensure Flink-powered

feature computation pipelines are delivered to production.

• Platform Security Delivery:



Lead engineering efforts for platform security spanning

OAuth 2.0, Conduktor Gateway, TLS, Kafka ACLs, multi-tenant isolation,

confidential compute (Azure Confidential Clean Rooms / Opaque Systems), and

differential privacy (Smart Noise/OpenDP) for cross-client analytics.

• Data Trust & Governance: Drive delivery of data contracts on Gold schemas,

pipeline validation (Great Expectations/Soda), end-to-end data lineage, automated

anomaly detection, and regulatory automation (PII classification, DORA, BCBS 239,

GDPR).

• Client Delivery Engineering: Lead engineering for multiple delivery patterns —

streaming SDK (Vanguard), batch extract (BMO), Mirror Maker 2, Web Socket/SSE

gateway, self-service client portal, and the Wealth-as-a-Service API (REST +

GraphQL).

• Cross-Client Analytics: Drive engineering delivery of the three-layer privacy stack

— federated processing (data never leaves client boundary), confidential compute

(hardware-attested enclaves), and differential privacy on all outputs. Lead federated

learning implementation using federated learning frameworks.

• Stream Processing Engineering: Lead the dual-engine strategy — Kafka Streams

for CDC processing and enrichment, Apache Flink for analytical stream processing

(windowed aggregations, complex event patterns, streaming SQL). Drive

performance optimization and operational stability.

• Technology Evaluation & Selection: Lead build-vs-buy decisions across the

platform — data lineage (Atlan vs. Purview vs. custom), observability (Monte Carlo

vs. custom), confidential compute (Opaque Systems vs. Azure Clean Rooms),

developer portal (Backstage vs. custom). Own proof-of-concept delivery and vendor

evaluation.

• Team Leadership: Lead and mentor data engineers, platform engineers, and

specialists across the data platform. Set engineering standards, conduct code and

design reviews, and foster a high-performance engineering culture.

• Stakeholder Management: Work with product owners and executive stakeholders

to translate roadmap priorities into engineering plans, align delivery timelines with

client commitments (Vanguard, BMO, RJ), and communicate progress and risks.

• Engineering Excellence:



Establish and enforce engineering standards — CI/CD

practices (Git Hub Actions, ArgoCD), testing strategies, observability

(Grafana/Prometheus), incident response, and operational runbooks across all

platform teams.

Qualifications:

• Education: Bachelor's or Master's degree in Computer Science, Engineering, or a

related technical field.

• Experience: 10+ years of experience in software/data engineering, with at least 5

years leading engineering teams delivering large-scale data platforms.

• Streaming Platforms: Deep hands-on expertise with Apache Kafka — topic design,

partitioning strategies, Kafka Streams, Kafka Connect, schema registries, and CDC

patterns (Debezium).

• Analytical Platforms: Robust experience building and delivering modern lakehouse

platforms — Microsoft Fabric, Delta Lake, Apache Iceberg, Parquet, and semantic

layers and data transformation frameworks.

• Cloud & Infrastructure: Extensive experience delivering on Azure (AKS, One Lake,

Fabric, Key Vault, Managed Identities) with Kubernetes-based deployments.

• Platform Security: Deep understanding of OAuth 2.0, TLS, network segmentation,

multi-tenant isolation, and data encryption patterns in financial services

environments.

• AI/ML Platforms: Working knowledge of feature stores, RAG implementations,

vector databases, and ML serving infrastructure.

• Data Governance: Experience delivering data contracts, data lineage, data quality

frameworks, and regulatory compliance solutions (DORA, BCBS 239, GDPR).

• Engineering Leadership: Proven track record of leading cross-functional

engineering teams, delivering against roadmaps, managing technical debt, and

driving engineering excellence.

Preferred Qualifications:

• Experience working in the Wealth Management or Financial Services industry with

strong emphasis on data governance and regulatory compliance.

• Experience with privacy-preserving technologies — confidential compute,

differential privacy, federated learning.

• Hands-on experience with Apache Flink for analytical stream processing alongside

Kafka Streams.

• Experience with Git Ops (ArgoCD), Helm umbrella charts, and platform engineering

practices (Backstage).

• Track record of delivering platforms that serve multiple clients with distinct

delivery patterns (streaming, batch, API).

• Experience with agile delivery at scale — sprint planning, backlog management,

cross-team coordination, and delivery reporting.

• Relevant certifications (Azure, Confluent Kafka) are a plus.

📌 Platform Engineering Lead (India)
🏢 FNZ Group
📍 India