Job description

About the role
dopay is digitizing payroll for businesses across Egypt and helping workers access their earnings with dignity. As we scale, our Revenue and Operations teams need a single source of truth - one person who owns how we measure performance, where the data lives, and what it tells us to do next.
This is that role.
You'll be the analytical backbone for two of dopay's most strategic functions. You'll design the KPIs we run the business on, build the Looker dashboards that surface them, and own the semantic layer in LookML so that "active customer," "revenue," and "operational throughput" mean the same thing in every meeting. You'll work side-by-side with Revenue leaders to understand what's working in the funnel, and with Operations to find friction in onboarding, processing, and support. You'll partner closely with our Engineering and Data teams to ensure the BigQuery layer is clean, complete, and accessible.
This is not a role for someone who waits for tickets. We need someone who walks into a Revenue weekly, sees a number trending sideways, and shows up Monday with three hypotheses and the data to test them.
What you'll own

• The KPI framework for Revenue and Operations. Define, document, and maintain the metrics that matter - from acquisition and activation through retention, expansion, and operational SLAs. Get cross-functional alignment on definitions and defend them.
• The Looker semantic layer (LookML). Build and maintain explores, views, and derived tables so business users can self-serve without breaking things. Treat the model like production code.
• Dashboards that drive action, not just inform. Every dashboard should answer a clear business question and surface what to do next. No vanity metrics. No 40-tile graveyards.
• Deep-dive analysis. Funnel diagnostics, cohort behavior, pricing and discount effectiveness, operational bottleneck analysis, customer health scoring. Bring hypotheses, not just charts.
• The data partnership with Engineering. Work with the tech team to ensure all relevant data flows into BigQuery cleanly and on time. Spec what's missing. Catch what's broken. Advocate for instrumentation when product decisions outrun our data.
• Stakeholder enablement. Train Revenue and Ops leaders to use Looker confidently. Reduce ad-hoc requests by making self-service genuinely possible.
• Forecasting and planning support. Partner on revenue forecasts, capacity planning, and goal-setting with grounded, defensible numbers.

• 4+ years in business analytics, analytics engineering, or revenue/operations analytics, ideally in fintech, B2B SaaS, or a high-growth operational business
• Demonstrated ownership of a BI environment end-to-end - not just dashboard authoring, but data modeling and stakeholder partnership

• Expert SQL. You can write performant queries against BigQuery without thinking twice
• Strong Looker experience required, including LookML development (explores, views, derived tables, persistent derived tables, access controls)
• Comfort with BigQuery: partitioning, cost-aware querying, scheduled queries
• Bonus: dbt, Python for analysis, Git-based workflows for LookML version control

• You understand revenue mechanics - funnels, cohorts, retention, unit economics - and can connect a metric to a decision
• You're comfortable in operations conversations: SLAs, throughput, error rates, capacity
• You ask "so what?" before you ask "how do I chart this?"

• You can explain a cohort retention curve to a non-technical commercial leader without losing them, and to an engineer without insulting them
• You write clear documentation and you actually maintain it
• Egyptian Arabic and English fluency strongly preferred for stakeholder work

• Self-directed. You don't need a ticket queue to know what's important
• Opinionated about data quality, naming conventions, and KPI hygiene - and diplomatic about defending those opinions
• Energized by ambiguous problems in fast-moving environments

• This is not a junior data analyst role. We need someone who has owned analytics for a business function before
• This is not a pure data engineering role. You'll partner closely with Engineering, but you won't own pipelines, infrastructure, or warehouse architecture
• This is not a data science role. There's room for predictive work over time, but the immediate need is descriptive and diagnostic excellence