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Platform Engineering Lead

LocationPune Job Posting Location - India

Typefull-time

DepartmentEngineering

Company size1,001–5,000 people

First seen2w ago

Last seen4d ago

About the role

Job Title: Platform Engineering Lead (FNZ)

About the company

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.

Responsibilities

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 (KeyCloak),

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 OneLake, 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 (SmartNoise/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), MirrorMaker 2, WebSocket/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 (GitHub Actions, ArgoCD), testing strategies, observability

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

platform teams.

Requirements

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: Strong 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, OneLake,

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.

Nice to have

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 GitOps (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.

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.*

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