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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
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
— 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.
event-driven platform comprising 179 Kafka Streams topologies, Debezium CDC
pipelines, 200+ Avro schemas (Apicurio Registry), OAuth 2.0 security (KeyCloak),
and Kubernetes-based deployment. Drive performance tuning, reliability
improvements, and feature delivery.
Warehouse on Microsoft Fabric, including Kafka-to-Fabric Direct Sink,
Delta/Parquet storage on, semantic layer, and future Apache Iceberg
adoption for time-travel queries and multi-engine access.
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.
OAuth 2.0, Conduktor Gateway, TLS, Kafka ACLs, multi-tenant isolation,
confidential compute (Azure Confidential Clean Rooms / Opaque Systems), and
differential privacy (SmartNoise/OpenDP) for cross-client analytics.
pipeline validation (Great Expectations/Soda), end-to-end data lineage, automated
anomaly detection, and regulatory automation (PII classification, DORA, BCBS 239,
GDPR).
streaming SDK (Vanguard), batch extract (BMO), MirrorMaker 2, WebSocket/SSE
gateway, self-service client portal, and the Wealth-as-a-Service API (REST +
GraphQL).
— 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.
for CDC processing and enrichment, Apache Flink for analytical stream processing
(windowed aggregations, complex event patterns, streaming SQL). Drive
performance optimization and operational stability.
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.
specialists across the data platform. Set engineering standards, conduct code and
design reviews, and foster a high-performance engineering culture.
to translate roadmap priorities into engineering plans, align delivery timelines with
client commitments (Vanguard, BMO, RJ), and communicate progress and risks.
practices (GitHub Actions, ArgoCD), testing strategies, observability
(Grafana/Prometheus), incident response, and operational runbooks across all
platform teams
related technical field.
years leading engineering teams delivering large-scale data platforms.
partitioning strategies, Kafka Streams, Kafka Connect, schema registries, and CDC
patterns (Debezium).
platforms — Microsoft Fabric, Delta Lake, Apache Iceberg, Parquet, and semantic
layers and data transformation frameworks.
Fabric, Key Vault, Managed Identities) with Kubernetes-based deployments.
multi-tenant isolation, and data encryption patterns in financial services
environments.
vector databases, and ML serving infrastructure.
frameworks, and regulatory compliance solutions (DORA, BCBS 239, GDPR).
engineering teams, delivering against roadmaps, managing technical debt, and
driving engineering excellence
strong emphasis on data governance and regulatory compliance.
differential privacy, federated learning.
Kafka Streams.
practices (Backstage).
delivery patterns (streaming, batch, API).
cross-team coordination, and delivery reporting.
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.
How to apply for Platform Engineering Lead at fnz?
Click the "Apply on Company Website" button on this page to submit your application directly on the employer's official portal.
What is the salary for this role?
The salary for this role is $1 per annum.
What experience is required?
10+ years of experience is required.
Is this position still open?
Yes, currently active and accepting applications.
Platform Engineering Lead
fnz · Pune Job Posting Location - India
