The Senior Data Architect is responsible for designing and delivering enterprise-scale cloud data architectures aligned with business objectives and regulatory requirements.

This role leads data architecture initiatives across pre-sales and delivery, covering data modelling, integration, governance, and cloud data platforms. It also enables AI/ML and Generative AI solutions by building scalable, secure, and high-performance data foundations while providing technical leadership and architectural guidance to engineering teams and clients.

Responsibilities:

• Define target-state data architectures, including data lakehouse, data warehouse, streaming, and Master Data Management (MDM) solutions.
• Lead architecture workstreams across pre-sales and delivery, including discovery workshops, current-state assessments, solution blueprints, and effort estimation.
• Design conceptual, logical, and physical data models and integration patterns for batch, micro-batch, and real-time data pipelines.
• Establish and implement data governance frameworks covering data quality, lineage, cataloguing, security, and access control.
• Guide engineering teams on implementation standards, architecture reviews, code reviews, and data platform performance optimization.
• Design data foundations for AI/ML and Generative AI use cases, including feature stores, vector databases, and Retrieval-Augmented Generation (RAG)-ready data pipelines.
• Act as a trusted technical advisor to client Chief Data Officers (CDOs), heads of data, and enterprise architects.
• Present and justify architecture decisions to technical and executive stakeholders.
• Develop reusable accelerators, reference architectures, and data practice standards.

Our Culture & Code of Conduct:

At ZainTECH, we take pride in a culture built on collaboration, innovation, and uncompromising integrity. We are looking for individuals who share these values and are committed to customer-centricity and ethical excellence. All employees are expected to uphold our Code of Conduct, which serves as a guiding framework for responsible behavior across everything we do — from ho we work with each other to how we engage with clients and partners globally.

Requirements

• 8+ years of experience in data engineering or data platform roles, including at least 4 years as a Data Architect on enterprise-scale programmes.
• Proven experience designing and delivering end-to-end cloud data platforms covering ingestion, storage, transformation, serving, and consumption.
• Strong expertise in data modelling methodologies, including Kimball/dimensional modelling, Data Vault, and Third Normal Form (3NF).
• Advanced SQL skills and experience with distributed data processing technologies such as Apache Spark.
• Experience with modern lakehouse table formats, including Delta Lake, Apache Iceberg, or Apache Hudi.
• Strong understanding of data governance, security, and privacy practices, including RBAC, encryption, masking, personally identifiable information (PII) handling, and regulatory compliance.
• Proficiency in Python for data engineering and automation, with an understanding of CI/CD and infrastructure-as-code concepts.
• Experience with streaming technologies such as Apache Kafka, Azure Event Hubs, or equivalent platforms.
• Excellent English communication skills, with the ability to translate business requirements into technical architectures and justify design decisions.
• Consulting or client-facing delivery experience, preferably across multiple industries.

• Hands-on experience with Microsoft Fabric and/or Azure Synapse Analytics.
• Experience with Azure Databricks, including Unity Catalog, Delta Live Tables, and workspace administration.
• Knowledge of Azure Data Factory or Microsoft Fabric Data Pipelines for orchestration and integration.
• Experience designing Azure Data Lake Storage Gen2 architectures and data zones, including raw, curated, and consumption layers.
• Familiarity with Microsoft Purview for data governance, cataloguing, and lineage.
• Experience with Azure SQL, Cosmos DB, Azure Event Hubs, and Stream Analytics.
• Exposure to Azure AI services, including Azure OpenAI, Azure AI Search, vector search, and Azure Machine Learning.
• Knowledge of Azure landing-zone and platform design, including networking, private endpoints, Microsoft Entra ID, Key Vault, and cost management.
• Relevant Microsoft certifications, such as DP-600, DP-203, or AZ-305.
• Experience with the Cloudera platform, including CDP, Cloudera Data Engineering, HDFS, Hive, Impala, NiFi, and Ranger, across on-premises or hybrid deployments.