Backend Engineer
APIs, data modelling, reliability and incident handling for product backends.
APIs, data modelling, reliability and incident handling for product backends.
SQL, analysis, dashboards and turning numbers into decisions.
Dashboards, SQL reporting, KPI definitions and helping teams act on data.
Web front ends, APIs, databases and shipping complete features end to end.
A technical screen on API design, data access, debugging, testing and practical backend trade-offs under realistic load.
Analytical thinking, data quality, basic spreadsheet reasoning, communication and commitment to project-based learning.
Python services, REST APIs, database queries, validation and systematic debugging of backend faults.
Statistical foundations, coding experience, project reasoning and motivation for advanced study in data science.
Business-led analysis, SQL, data quality, dashboard design and clear recommendations for client decisions.
Fraud pattern analysis, rule performance, loss monitoring, experiment design and clear recommendations from transaction data.
Building reliable Power BI models, measures and dashboards from business data and user requirements.
Reconciling payment records across internal ledgers, processors and bank statements, then investigating breaks and protecting financial accuracy.
Funnel analysis, experiment design, event quality and translating ecommerce behaviour into product decisions.
Cleaning research datasets, selecting suitable analyses, producing clear visualisations and communicating limitations.
Analysis of inventory, demand, supplier and delivery data to explain service gaps, improve planning and support operational decisions.
Warehouse data modelling, dbt project design, data quality tests and trustworthy business metrics.
Ledger integrity, account services, transaction processing, migrations and safe changes to core banking systems.
Commercial analytics leadership across conversion, customers, marketing, merchandising and fulfilment performance.
Validating interval data, analysing consumption and losses, explaining anomalies and producing useful operational dashboards.
Designing fraud controls, analysing attack patterns, measuring rule performance and balancing loss reduction with customer approval.
Analysing shopping funnels, investigating product changes and turning customer behaviour data into clear product recommendations.
Cleaning logistics data, defining delivery metrics, finding operational causes and communicating useful insights.
Operations support integrations, alarm and performance data, inventory accuracy, automation and platform support.
Wallet funnel analysis, transaction behaviour, experimentation, metric design and diagnosis of customer drop-off.
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