About the role
Senior Data & AI Platform Engineer
At OrderYOYO, data powers executive reporting, payments, finance, merchant insights, product analytics, AI, marketing automation, and M&A integration. This role will shape the governed, increasingly AI-enabled data foundation that supports our next stage of scale.
Role mission
Own the continuity, evolution and AI-enablement of OrderYOYO’s modern data platform during a critical scaling phase. You will lead the migration from legacy reporting and metric tooling into a governed Microsoft Fabric platform, keep business-critical BI and semantic models reliable, improve data pipeline stability and monitoring, support CRM data integration, apply AI and automation to improve data engineering, reporting and analytics, and provide senior technical leadership for data engineering delivery.
Core responsibilities
Lead hands-on Microsoft Fabric architecture across lakehouse, warehouse, notebooks, semantic models, Git-backed delivery and production governance.
Drive migration from legacy reporting and metric tooling into a governed Fabric semantic layer, including parity testing, stakeholder sign-off and safe decommissioning.
Own and improve data pipelines across APIs, files, events and operational stores; establish robust orchestration, monitoring, alerting, data-quality checks and incident response.
Use AI and automation to accelerate ETL/ELT development, data mapping, documentation, testing, report generation, monitoring and data-quality management.
Design high-quality Power BI semantic models, DAX measures and reusable metric definitions for leadership, finance, commercial, product, marketing, payments and support reporting.
Support CRM and operational data integrations, including outbound data feeds, identity mapping, schema mapping, reverse-ETL patterns and monitoring.
Create reliable ingestion and modelling patterns for acquired businesses, so future integrations are repeatable, auditable and faster to execute.
Set data-engineering standards: definition of ready/done, code review, release discipline, documentation, runbooks and platform change governance.
Mentor engineers and analysts and translate business-critical data needs into pragmatic technical delivery.
Build automated reporting and insight-generation capabilities that reduce manual analysis and improve decision speed.
Must-have requirements
6+ years in modern data warehousing, analytics engineering or data platform engineering, ideally in a SaaS, marketplace, fintech, payments, e-commerce or multi-region B2B2C environment.
Strong Microsoft Fabric capability, or deep Azure Synapse / Databricks experience with clear ability to specialise quickly in Fabric.
Expert SQL/T-SQL plus strong Python or PySpark, with a track record of building maintainable ELT/ETL pipelines and analytical data models.
Strong Power BI and DAX experience, including semantic modelling, incremental refresh, performance tuning, model governance and capacity/cost awareness.
Experience leading legacy-to-modern data platform migrations, including metric parity, stakeholder validation, change control and safe decommissioning.
Experience operating production data systems: monitoring, alert design, incident triage, root-cause analysis, data-quality checks, lineage and runbooks.
Comfortable with Git-based data engineering workflows, pull requests, release discipline and standards for notebooks, pipelines and semantic model changes.
Practical experience using AI or automation to improve data engineering, reporting, documentation, testing, monitoring, migration or developer productivity.
Strong-to-have experience
Payments, settlement, reconciliation, fees, chargebacks, merchant reporting or finance-domain data.
CRM-side data flows and reverse-ETL patterns, especially HubSpot, Salesforce, Zendesk or similar platforms.
M&A or acquired-company data integrations: schema discovery, file/API ingestion, data profiling, master-data mapping, migration QA and reporting continuity.
NoSQL-to-analytics modelling, including change-feed patterns from operational databases into lakehouse or warehouse structures.
GA4, BigQuery export, Google Ads / SEM feeds, Segment or other event and marketing analytics sources.
Experience with Azure OpenAI, LLMs, RAG, AI agents, prompt/version management or AI-assisted development workflows.
Experience building AI-generated reporting, natural-language analytics, business copilots, automated insight generation or merchant/customer intelligence tools.
Responsible AI and governance experience, including RBAC, PII handling, audit logs, human approval flows, explainability and GDPR-conscious design.
Candidate signals to prioritise in interview
Has owned a production data platform, not only built dashboards or one-off analytics projects.
Can explain how they governed metrics and prevented conflicting definitions across teams.
Has migrated or consolidated legacy reporting into a modern semantic layer without breaking business trust.
Balances delivery urgency with reliability, documentation, cost control and operational resilience.
Communicates clearly with executives, product teams, analysts and engineers; can say “no” or “not yet” with evidence.
Is hands-on enough to debug pipelines and models, while senior enough to set standards and mentor others.
Has used AI or automation in a real data-engineering context to speed up delivery, not just as a novelty, and can describe the guardrails they put around it.
Originally posted on Himalayas