Principle Data Architect

FNZ (UK) Ltd; Pune District; On-site; INR 2,500,000 - 3,500,000; Full time

Job summary

FNZ (UK) Ltd is looking for a Senior Data Architect to design and own the architecture of the Analytical Warehouse built on Microsoft Fabric. This role includes defining data models, ingestion patterns, and governance framework to transform streaming data into a structured analytical platform.

The ideal candidate will have 8+ years of experience, strong SQL skills, and a deep understanding of data architecture in the financial services sector.

Qualifications

• 8+ years of experience in data architecture or data engineering.
• At least 3 years in a data architect role on analytical/warehouse platforms.
• Advanced SQL skills for analytical queries and performance tuning.

Responsibilities

• Design and own the architecture of the Analytical Warehouse.
• Define data models, storage strategies, and governance framework.
• Architect ingestion pipelines and batch extract migration.

Skills

Data architecture

Microsoft Fabric

Dimensional modeling

SQL

Data governance

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

Azure Synapse Analytics

Kafka

Data catalogs (Purview)

Job description

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 senior Data Architect to design and own the architecture of the Analytical Warehouse built on Microsoft Fabric. This role is responsible for defining the data models, storage strategies, ingestion patterns, semantic layer, and governance framework that transform the NRT‑ODS Gold‑layer streaming data into a structured, performant, and governed analytical platform. You will architect the bridge between real‑time streaming and historical analytics, serving both operational BI and client‑facing reporting workloads.

Key Responsibilities

• Analytical Warehouse Architecture: design end‑to‑end architecture for the Analytical Warehouse on Microsoft Fabric — ingestion from ODS Gold topics, Bronze/Silver/Gold layering within OneLake, transformation pipelines, semantic layer, and consumption endpoints.
• Data Modelling: define dimensional models, star schemas, and wide denormalized tables optimized for analytical query patterns. Design fact and dimension tables for wealth management domains — accounts, portfolios, transactions, positions, fees, NAV, AUM.
• Ingestion Architecture: architect the Kafka‑to‑Fabric ingestion pipeline — Kafka Connect sink configuration, Avro‑to‑Delta schema mapping, partitioning strategy (date, entity type, client), exactly‑once delivery semantics, and error handling.
• Lakehouse Strategy: define OneLake storage architecture including namespace design, table format strategy (Delta Lake short‑term, Apache Iceberg long‑term), partition evolution, file compaction policies, and retention management.
• Semantic Layer Design: architect semantic layer that provides business‑friendly metrics (AUM, NAV, trade volumes, fee breakdowns) with consistent definitions across dashboards, reports, APIs, and client portals.
• Data Sharing Architecture: design architecture for Fabric Data Sharing — OneLake shortcuts and Delta Sharing protocols that enable clients to consume analytics in their own Fabric tenants with governed, client‑scoped access.
• Data Governance & Contracts: extend ODS data contracts framework into Analytical Warehouse. Define governance policies for the analytical layer including data classification, access controls (Purview), lineage tracking, and audit trails.
• Batch Extract Migration: architect the migration of batch extract from SQL‑driven CSV to Kafka‑sourced Parquet/Delta via Fabric pipelines. Design metadata‑driven configuration that preserves CentralHub flexibility.
• Performance Architecture: design for query performance — Z‑ordering strategies, partition pruning, materialized views, caching layers, and compute resource allocation across Fabric workspaces.
• Apache Iceberg Roadmap: plan long‑term migration to Apache Iceberg on OneLake for time‑travel queries, partition evolution, and multi‑engine access (Fabric, Spark, Trino, Flink). Evaluate Confluent Tableflow or custom sink for Kafka‑to‑Iceberg pipeline.
• Standards & Governance: establish naming conventions, modelling standards, documentation requirements, and code review processes for all Analytical Warehouse development. Conduct architecture reviews for Data Engineer deliverables.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related technical field.
• 8+ years of experience in data architecture or data engineering, with at least 3 years in a data architect role on analytical/warehouse platforms.
• Deep experience with Microsoft Fabric, Azure Synapse Analytics, or equivalent cloud analytical platforms. Strong understanding of OneLake, Fabric lakehouse, and Fabric SQL endpoints.
• Expert‑level skills in dimensional modeling (Kimball), data vault, and denormalized modeling for analytical workloads. Experience modeling financial services data domains.
• Strong understanding of modern table formats — Delta Lake (ACID transactions, time travel, schema evolution) and Apache Iceberg (partition evolution, multi‑engine support).
• Advanced SQL skills for analytical queries, performance tuning, and query plan analysis.
• Experience architecting data pipelines that bridge real‑time streaming platforms (Kafka) with analytical warehouses/lakehouses.
• Experience with semantic layer and data transformation tools for defining governed business metrics.
• Experience with data governance frameworks, data catalogs (Purview, Atlan), and access control policies in multi‑tenant environments.

Preferred Qualifications

• Experience working in the Wealth Management or Financial Services industry with deep understanding of investment operations data models.
• Experience with Apache Kafka — consumer architecture, Kafka Connect, Avro schema evolution, and schema registries.
• Familiarity with SQL‑based transformation frameworks for managing transformation layers (models, tests, documentation, CI/CD).
• Experience with data quality frameworks (Great Expectations, Soda) integrated into analytical pipelines.
• Experience architecting multi‑tenant analytical platforms with client‑scoped data isolation.
• Knowledge of privacy‑preserving analytics — differential privacy, confidential compute, or federated analytics patterns.
• Microsoft Fabric certifications, Azure Data Engineer (DP‑203), or Azure Solutions Architect certifications.