Platform Engineering Lead

FNZ (UK) Ltd; Pune District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

FNZ (UK) Ltd is looking for a Platform Engineering Lead in Pune, India, to drive the engineering delivery of its data platform. You will lead efforts across multiple engineering pillars including streaming, analytical capabilities, and platform security, ensuring quality and performance standards are met.

The ideal candidate should have over 10 years of experience, including at least 5 in leadership roles within large-scale data platforms. Strong expertise in Apache Kafka and Azure deployment is essential for success in this position.

Qualifications

• 10+ years of software/data engineering experience.
• 5+ years of leading engineering teams for large-scale platforms.
• Expertise in OAuth 2.0 and platform security.

Responsibilities

• Lead engineering delivery for the data platform.
• Drive performance tuning and reliability improvements.
• Establish engineering standards across platform teams.

Skills

Apache Kafka expertise

Cloud & Azure experience

Data governance and compliance

Engineering leadership

Analytical platforms

Education

Bachelor's or Master's degree in Computer Science or Engineering

Tools

Microsoft Fabric

Kubernetes

Apache Flink

Job description

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 (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.

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.

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.