GCP Analyst - Finance

Drive business value through procurement and spend analytics, cloud-based data solutions, reporting automation, AI-enabled insights, and strong data governance. The role combines hands-on technical delivery with business analysis, stakeholder engagement, migration support, validation, and production ownership.

Data analytics and business insights

• Generate actionable insights using advanced analytics and information-visualization techniques to solve business problems.
• Analyze procurement, spend, supplier, invoice, purchase-order and card-related data to identify trends, anomalies and improvement opportunities.
• Validate, interpret and present findings to technical and non-technical stakeholders, including senior leadership.

Analytics engineering and automation

• Develop, maintain and optimize solutions using Python, PySpark, SQL/HQL, BigQuery and Alteryx.
• Automate data preparation, mapping, reconciliation, notifications, monitoring, reporting and repeatable BAU workflows.
• Own scheduled jobs and production use cases, including deployment, validation, troubleshooting, documentation and service continuity.

AI and GenAI enablement

• Identify and deliver practical AI and GenAI use cases for analytics, anomaly detection, knowledge retrieval, summarization and process productivity.
• Apply prompt-engineering fundamentals and responsible AI practices, including human validation, data privacy, security, fairness and transparency.
• Use Python and approved AI tools to prototype, evaluate and operationalize business solutions; define success measures and monitor output quality.

Data platforms, migration and governance

• Support migration of analytical codes, dashboards, data assets and ingestion processes to cloud platforms such as Lumi/BigQuery.
• Perform feasibility assessments, impact analysis, UAT/DVT, reconciliation, root-cause analysis and business sign-off support.
• Maintain SOPs, process guides, control checks, data lineage, access dependencies and governance documentation.

Reporting, controls and stakeholder management

• Own or support critical reporting and mapping processes, including spend analytics, supplier analytics and compliance monitoring.
• Design and maintain KRIs, governance checks and automated alerts for data or process anomalies.
• Partner with business, product, technology, compliance and operations teams; lead requirement gathering, knowledge transfer and user enablement.

Required skills and qualifications

• Bachelor’s degree in engineering, computer science, statistics, mathematics, business analytics or a related field.

• 4–5 years of relevant experience in data analytics, business intelligence, reporting automation or analytics consulting.

• Strong procurement process and business knowledge; experience with P2P or spend analytics is preferred.

• Strong hands-on knowledge of Python, Pandas, PySpark, SQL/HQL and cloud data warehouses such as BigQuery.

• Strong Alteryx capability covering workflow development, data blending, transformation, validation, optimization and scheduling.

• Working knowledge of AI/ML and GenAI concepts, prompt design, AI-assisted analytics, model/output evaluation and responsible AI controls.

• Experience with Tableau capabilities, including dashboard development and interactive analytics; Power BI knowledge is an advantage.

• Advanced Excel and PowerPoint skills, with strong analytical, troubleshooting and presentation capability.

• Experience managing enterprise-level initiatives and enhancing or automating reporting structures.

• Ability to work autonomously, make sound decisions under ambiguity and time pressure, and collaborate effectively across teams.

• Excellent written and verbal communication skills, with the ability to explain technical findings to non-technical audiences.

• Curiosity and willingness to learn new open-source technologies, languages and AI-enabled methods.

Preferred experience

• Cloud analytics platforms, code repositories, deployment pipelines and scheduled workflow management.

• Data migration, cutover, UAT/DVT, reconciliation and production stabilization.

• Data privacy, PII handling, access governance, controls and compliance reporting.

• Process documentation, SOP creation, transition planning and hands-on knowledge transfer.

Success measures

• Reliable continuity of critical reporting, mapping and monitoring processes.

• Measurable reduction in manual effort through Python, Alteryx and AI-enabled automation.

• Accurate, well-governed and timely analytical outputs with clear validation evidence.

• Successful migration and stabilization of data assets, codes and dashboards.

• Clear documentation, effective stakeholder communication and sustainable knowledge transfer.