Data Architect

The Data Architect will define, design, and govern the data architecture for Billing end-to-end process, enabling scalable, secure, and business-aligned data platforms.

Key Responsibilities:

Enterprise Data Architecture

• Define and maintain the data architecture roadmap aligned with business and technology strategies.
• Design scalable and reusable data architectures, including operational, analytical, and reporting data layers.
• Establish enterprise data models, conceptual, logical, and physical data models, ensuring consistency across domains.
• Develop and maintain dimensional models, star schemas, and canonical data models to support reporting and analytics requirements.
• Drive architecture standards, design patterns, and best practices for data platforms and integrations.

Data Acquisition & Integration

• Design and govern data ingestion, integration, and transformation frameworks using native AWS tools like Glue, Air Flow, and Snowflake, Data Bricks as Data Store.
• Architect data pipelines and integration patterns for structured, semi-structured, and unstructured data sources.
• Ensure end-to-end data lineage, reconciliation, and traceability across all billing process data flows.
• Define standards for data quality, validation, and data synchronization across platforms.
• Optimize data movement and transformation processes for performance, scalability, and cost efficiency.

Cloud Data Platform & Warehousing

• Architect enterprise data solutions leveraging Snowflake and cloud platforms including AWS.
• Define data storage, compute, security, and data lifecycle management strategies.
• Design modern data warehouse, data lake, and lakehouse architectures to support enterprise-wide reporting and analytics.
• Establish standards for data partitioning, clustering, performance optimization, and workload management.
• Collaborate with infrastructure and engineering teams to ensure resilient and scalable data environments.

Reporting & Analytics Enablement

• Partner with reporting and analytics teams to ensure data architectures support business intelligence and self-service analytics.
• Define and maintain certified data products and semantic layers for reporting tools such as Power BI.
• Ensure data models are optimized for analytical consumption and regulatory reporting requirements.
• Enable consistent metrics, KPIs, and master data definitions across business functions.

Governance, Metadata & Security

• Lead implementation of enterprise data governance frameworks, standards, and policies.
• Establish and manage metadata management, business glossaries, and data catalog capabilities.
• Define data security architecture, including role-based access controls, encryption, masking, and regulatory compliance requirements.
• Ensure adherence to audit, risk, privacy, and regulatory standards.
• Promote data stewardship and ownership across business and technology teams.

Data Operations & Orchestration

• Define standards for workflow orchestration and scheduling using Control-M or equivalent enterprise scheduling tools.
• Design monitoring frameworks for data quality, pipeline reliability, and operational excellence.
• Ensure high availability, disaster recovery, and business continuity considerations are embedded into data architecture designs.

Required Skills & Experience:

• Extensive experience in Data Architecture, Data Warehousing, and Enterprise Data Modeling.
• Strong expertise in Snowflake architecture, administration, and performance optimization.
• Experience with ETL/ELT platforms such as AWS Glue, Airflow, Ab Initio, Matillion, DataStage, or comparable technologies.
• Deep understanding of dimensional modeling, star schema, data vault, and enterprise data modeling techniques.
• Hands-on experience with AWS and/or Azure data services.
• Strong knowledge of data governance, metadata management, data lineage, and data quality frameworks.
• Experience defining architecture standards, integration patterns, and technology roadmaps.
• Understanding of security, compliance, and risk management principles for enterprise data environments.
• Strong stakeholder management, communication, and documentation skills.

Benefits:

• Equal opportunity employer with hybrid working arrangements.
• Annual Leave and Parental Leave benefits.
• Upskilling opportunities through role-relevant training and certifications.
• Dedicated Employee Assistance and Wellbeing Programs.
• Opportunity to work on enterprise-scale data transformation and analytics initiatives.
• Collaborative environment focused on innovation, growth, and continuous learning.