Engineering Manager
Mrsool
IndiaremotePosted 2 months ago
Skill Required
Engineering-ManagementSoftware-Engineering-LeadershipBackend-EngineeringDistributed-Systems-EngineeringTechnical-ManagementEngineering-ManagerIT-Engineering-ManagerSoftware-Engineering-ManagerPrincipal-Engineering-ManagerEngineering ManagerEngineeringsoftware engineeringSystem DesignMicroservicesObservabilityStatisticsanalyticalbuildingPythondesignCI/CDDesign PatternsRubyUnitySQLandGoCICDMachine LearningFulltime
Key highlights
- Competitive compensation with potential share options
- 8+ years software engineering experience, 2+ years managing teams
- Hands-on, data-driven leadership role with team of 8–10 engineers
- Core requirement: strong data science and analytical strength with experimentation/causal inference
- High-scale distributed systems, real-time event processing, microservices
- Flexible remote work environment
Role overview
Engineering Manager responsible for leading a team of 8–10 backend and distributed systems engineers at Mrsool, guiding the design, implementation, and operational excellence of real‑time, high‑scale services that power ordering, dispatch, payments, and user experiences across the platform. The role is a hands‑on, data‑driven leadership position that blends technical direction with cross‑functional collaboration, data science partnership, and strategic architecture to drive reliability, unit economics, and platform growth.
Responsibilities
- Lead, grow, and retain a team of 8–10 engineers; own hiring, performance management, career development, and delivery cadence
- Set technical direction for the domain, driving RFCs, design documents (HLD/LLD), and execution plans while maintaining high engineering standards
- Ground team decisions in data: define metrics, enforce measurement before and after changes, and use experimentation (A/B and quasi‑experimental) to validate impact
- Partner fluently with data scientists and ML systems for forecasting, optimisation, personalisation, and prediction, challenging modelling choices and integrating models reliably into production
- Guide the design of highly concurrent, low‑latency systems that process massive real‑time throughput, safeguarding data consistency and performance
- Own reliability and root‑cause discipline for the domain, champion staging/E2E validation, observability (tracing/metrics), and CI/CD to catch issues early and diagnose problems with data
- Collaborate closely with product, operations, data science, and peer engineering squads to align strategy and deliver seamless, well‑instrumented experiences
- Balance short‑term delivery against long‑term architectural stability, identify structural improvements, and make evidence‑based cases for change