Senior Data Architect- Azure / Fabric / CI/CD- Banking Industry

For one of our projects for a company in the

Banking Industry and to perform onsite in Zurich, NTT DATA is looking for a Senior Data Analytics.

• As a
• Senior Data Architect

, you will provide hands‑on technical leadership defining and evolving the enterprise data architecture for analytical and operational use cases, with a strong focus on

• Microsoft Azure and
• Microsoft Fabric

You will partner with data engineering, analytics, security and platform teams to design scalable, governed and cost‑efficient solutions – covering ingestion, transformation, storage, semantic modeling and consumption – ensuring data is trusted, secure and accessible.

This role is purely technical and requires strong engineering and architecture depth.

• Functions (duties and responsibilities)
• Define target data architecture, standards and reference patterns for Azure and Microsoft Fabric (Lakehouse/Warehouse, semantic models, data products).
• Design end‑to‑end data solutions: ingestion, orchestration, transformation, storage and serving layers, aligning with business and regulatory requirements.
• Own conceptual, logical and physical data models; drive canonical models, dimensional models and domain‑oriented data products.
• Establish governance‑by‑design: data classification, lineage, quality controls, metadata management and stewardship processes.
• Define security and access patterns (RBAC/ABAC), encryption and network isolation for data platforms, partnering with IAM and security teams.
• Guide implementation choices across Azure data services and Fabric capabilities, balancing performance, cost, resiliency and operability.
• Collaborate with the rest of the Engineering team to define pipelines, testing strategies, CI/CD and release governance for data assets.
• Drive adoption of best practices for semantic modeling and BI consumption (e. g., Power BI semantic models), ensuring consistency and reusability.
• Provide architectural leadership in design reviews, documenting decisions and producing technical evidence to support compliance needs.

Your profile

• Bachelor's or Master's degree in Computer Science, Software Engineering, Information Systems or a related technical field.
• 8+ years of experience in data engineering / analytics engineering / data platform roles, with 3+ years acting as Data Architect or in an architecture ownership capacity on enterprise‑scale platforms.
• Knowledge of Azure data services and architecture patterns (e. g., ADLS Gen2, Synapse, Data Factory, Event Hubs, Databricks) and the ability to select the right components for each use case, considering security, resiliency and cost.
• Hands‑on experience with Microsoft Fabric at production scale: One Lake, Lakehouse/Warehouse, Spark, Data Pipelines/Dataflows Gen2, semantic models, and workspace/tenant governance (including capacity considerations).
• Excellent SQL skills; experience with data modeling (dimensional, 3NF, Data Vault) and performance tuning for analytical workloads.
• Solid understanding of data governance, data quality, metadata/lineage, and privacy/security controls in regulated environments.
• Experience with metadata, cataloging and lineage tooling (e. g., Microsoft Purview) and with defining standards for documentation and discoverability.
• Strong engineering practices: version control, CI/CD for data assets, and Infrastructure as Code (e. g., Terraform/Bicep) to provision and operate Azure/Fabric data platforms.
• Experience defining architecture for integration patterns (batch, streaming, CDC, APIs) and designing scalable ingestion frameworks.
• Ability to read, understand and challenge technical designs, balancing business outcomes, time‑to‑market and long‑term maintainability.
• Excellent command of English and Spanish (German is a plus).
• The ideal candidate has experience in
• Defining data architecture roadmaps and target operating models for modern analytics platforms (lakehouse) on Azure/Microsoft Fabric.
• Designing governed data products, including ownership, SLAs, quality rules and documentation.
• Semantic modeling for self‑service BI and enterprise reporting (Power BI), including shared datasets/semantic models and reusable metrics.
• Implementing security patterns for data platforms (workspace/tenant governance, RLS/OLS, private connectivity) and aligning with IAM policies.
• Performance and cost optimization for large‑scale analytical workloads (capacity planning, partitioning, incremental processing, caching).
• Working in agile, multidisciplinary teams, leading architecture discussions and mentoring engineers on data design best practices.
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