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.