Data Architect

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About Us
Kyriba is a global leader in liquidity performance that empowers CFOs, Treasurers and IT leaders to connect, protect, forecast and optimize their liquidity. As a secure and scalable SaaS solution, Kyriba brings intelligence and financial automation that enables companies and banks of all sizes to improve their financial performance and increase operational efficiency. Kyriba's real-time data and AI-empowered tools empower its 3,000 customers worldwide to quantify exposures, project cash and liquidity, and take action to protect balance sheets, income statements and cash flows. Kyriba manages more than 3.5 billion bank transactions and $15 trillion in payments annually and gives customers complete visibility and actionability, so they can optimize and fully harness liquidity across the enterprise and outperform their business strategy. For more information, visit

*Position Summary*
We are seeking a visionary Data Architect / Platform Specialist with proven cross-enterprise domain experience to lead the design and implementation of robust, scalable data architectures across all business units. The ideal candidate will have deep expertise in Google BigQuery data design best practices and a track record of enabling advanced analytics, BI, ML, and Generative AI (GenAI) across large, diverse organizations. This role is pivotal in building a unified, high-performing data ecosystem that powers enterprise-wide insights and innovation.

*Key Responsibilities
Enterprise Data Architecture*

• Design, implement, and evolve data architectures that serve the entire enterprise, spanning multiple business domains and use cases.
• Define and enforce architectural standards and best practices for data modeling, integration, and governance.
• Ensure data solutions are scalable, secure, and optimized for performance across reporting, BI, advanced analytics, ML, and GenAI workloads.

*Google BigQuery Platform Leadership*

• Lead the implementation of BigQuery data design patterns and best practices, including partitioning, clustering, cost/performance optimization, and semantic modeling.
• Architect GCP data environments to support diverse data processing needs: batch, streaming, real-time, and advanced analytics using services such as Dataflow, Pub/Sub, Cloud Storage, and Cloud Composer.
• Guide teams in leveraging BigQuery for data engineering, BI, ML, and GenAI, ensuring seamless integration with Google Cloud Storage and other enterprise platforms.

*Cross-Functional Collaboration*

• Act as the primary interface between data, IT, business, and analytics teams to understand requirements and align data architecture with strategic goals.
• Drive data standardization and interoperability across domains (finance, operations, HR, supply chain, customer, etc.) to maximize business value.

*Reporting, BI, ML & GenAI Enablement*

• Architect and optimize data flows to support operational and analytical reporting, BI dashboards (e.g., Looker), and self-service analytics.
• Partner with Data Scientists and ML Engineers to ensure data architectures are ML/GenAI-ready, supporting feature stores, model training, and scalable inference (e.g., Vertex AI, BigQuery ML).
• Ensure data platforms can deliver both traditional reporting and next-generation GenAI solutions, including large language models and advanced AI workloads.

*Governance, Security, and Compliance*

• Implement enterprise data governance, data quality, security, and compliance frameworks.
• Oversee metadata management, data lineage, and cataloging to promote discovery and trust (e.g., Dataplex, Data Catalog).

*Innovation & Best Practices*

• Evaluate emerging technologies and drive adoption of innovative approaches in data management, analytics, ML, and GenAI.
• Foster a culture of continuous improvement and excellence in data architecture and platform engineering.

Requirements

• Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or related field.
• Extensive experience as a Data Architect or Platform Specialist supporting multiple business domains across large organizations.
• Proven expertise in designing and implementing data architectures on Google BigQuery and Google Cloud Storage.
• Deep knowledge of data modeling, data warehousing, ETL/ELT, and cloud data platforms.
• Experience with BigQuery best practices for reporting, BI, ML, and GenAI.
• Strong understanding of BI tools (e.g., Looker) and their integration with enterprise data platforms.
• Familiarity with ML/GenAI architectures, workflows, and operationalization (e.g., Vertex AI, BigQuery ML).
• Comprehensive knowledge of data governance, security, and compliance frameworks.
• Outstanding communication, leadership, and stakeholder management skills.

Nice to Have

• Certifications in Google Cloud (e.g., Professional Data Engineer, Professional Cloud Architect) or enterprise architecture frameworks (e.g., TOGAF).
• Experience with data mesh, data fabric, or modern data stack concepts.
• Exposure to automation and integration platforms (e.g., MuleSoft).