
Data Architecture and Engineering Lead
Responsibilities:
- Lead Data Architecture: Own the design, evolution, and delivery of enterprise data architecture across cloud and hybrid environments. Develop relational and analytical data models (conceptual, logical, and physical) to support business needs and ensure data integrity.
- Consolidate Core Systems: Unify data sources across airport systems into a single analytical platform optimised for business value.
- Build Scalable Infrastructure: Architect cloud-native solutions that support both batch and streaming data workflows using tools like Databricks, Kafka, etc.
- Implement Governance Frameworks: Define and enforce enterprise-wide data standards for access control, privacy, quality, security, and lineage.
- Enable Metadata & Cataloguing: Deploy metadata management and cataloguing tools to enhance data discoverability and self-service analytics.
- Operationalise AI/ML Pipelines: Lead data architecture that supports AI/ML initiatives, including forecasting, pricing models, and personalisation.
- Partner Across Functions: Translate business needs into data architecture solutions by collaborating with leaders in Operations, Finance, HR, Legal, Technology.
- Optimize Cloud Cost & Performance: Roll out compute and storage systems that balance cost efficiency, performance, and observability across platforms.
Qualifications:
- 12+ years of experience in data architecture, with 3+ years in a senior or leadership role across cloud or hybrid environments
- Proven ability to design and scale large data platforms supporting analytics, real-time reporting, and AI/ML use cases
- Hands-on expertise with ingestion, transformation, and orchestration pipelines
- Extensive experience with Microsoft Azure data services, including Azure Data Lake Storage, Azure Databricks, Azure Data Factory and related technologies.
- Strong knowledge of ERP data models, especially SAP and MS Dynamics
- Experience with data governance, compliance (GDPR/CCPA), metadata cataloguing, and security practices
- Familiarity with distributed systems and streaming frameworks like Spark or Flink
- Strong stakeholder management and communication skills, with the ability to influence both technical and business teams
Tools & Technologies
- Warehousing: Azure Databricks Delta, BigQuery
- Big Data: Apache Spark
- Cloud Platforms: Azure (ADLS, AKS, EventHub, ServiceBus)
- Streaming: Kafka, Pub/Sub
- RDBMS: PostgreSQL, MS SQL
- NoSQL: Redis
- Monitoring: Azure Monitoring, App Insight, Prometheus, Grafana

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