Technical Lead

This role sits at the intersection of application engineering and data engineering , with a strong focus on metrics, analytics infrastructure, and scalable data systems . You will lead the design of high-performance services as well as data pipelines and analytics layers powering business insights.\n\nAbout Nova\n\nTeam Nova builds the data and platform infrastructure that powers analytics, operational intelligence, and large-scale business workflows across Vymo. We work on high-scale backend and data engineering problems spanning:\n\nReal-time and batch data pipelines, Building resilient ingestion and ETL pipelines\nDesigning efficient OLAP schemas and large-scale analytical workloads in ClickHouse\nLarge-scale data transfer and synchronization between PostgreSQL and ClickHouse\nClickHouse data modeling and query optimization\nImproving performance, reliability, and scalability of data systems\nMetrics platforms, dashboards, and analytical systems\nJava-based microservices, distributed backend systems that power key metrics and data access\nHigh-volume event processing and operational observability\n\nOur team is responsible for designing scalable data architectures that enable fast, reliable, and actionable insights for both internal systems and customer-facing products.

We operate at the intersection of backend engineering, data infrastructure, and platform scalability. If you enjoy building scalable platforms, working with large datasets, and designing systems that directly influence product intelligence and business decision-making, Team Nova is the place for you.\n\nKey Responsibilities\n\nBackend & Platform Engineering\nDesign and implement scalable microservices using Java (Spring Boot) / Node.js .\nBuild high-performance APIs and distributed systems.\nImplement caching strategies (Redis/Memcached) to optimize latency and throughput.\nEnsure system reliability, scalability, and maintainability in production.\n\nData Engineering & Analytics Infrastructure\nDesign and implement data pipelines (batch & streaming) based on product and analytics requirements.\nBuild and optimize ClickHouse schemas and queries for large-scale analytics workloads:\nEfficient ingestion pipelines\nPartitioning and indexing strategies\nQuery performance tuning\nImplement data transformations and aggregation layers for downstream consumption.\n\nMetrics & Data Systems\nImplement systems for metrics computation and aggregation (real-time or batch).\nEnsure correctness, consistency, and efficiency of data processing pipelines.\nOptimize storage and retrieval for high-volume event data.\n\nDashboards & Data Access\nBuild and maintain Superset dashboards and underlying datasets.\nEnable efficient querying and visualization by structuring clean, performant data layers.\nWork with stakeholders to implement required reporting and analytics use cases.\n\nSystem Design & Performance\nLead technical design and architecture discussions .\nIdentify bottlenecks and optimize across:\nBackend services\nDatabases (OLTP & OLAP)\nData pipelines\nEnsure systems are designed for scale, fault tolerance, and observability.\n\nLeadership & Execution\nLead and mentor a team of engineers; drive execution and delivery.\nPerform code reviews and enforce engineering best practices.\nBreak down complex problems into actionable engineering tasks.\nCollaborate with product and analytics teams to implement requirements effectively.\n\nRequired Skills & Experience\n\n6–10+ years of experience in backend and/or data engineering.\nStrong expertise in:\nJava (Spring Boot, Microservices)\nNode.js (Express/NestJS or similar)\nHands-on experience with ClickHouse (or similar OLAP systems) .\nHands-on experience with Postgres (or similar OLTP systems) .\nExperience in building data pipelines and processing systems .\nStrong understanding of distributed systems and system design .\nExperience with:\nCaching (Redis, etc.)\nMessage queues (Kafka/RabbitMQ)\nSQL and query optimization\nExperience with Superset or similar BI tools .\n\nLeadership Expectations\n\nProven ability to lead small-to-mid sized engineering teams.\nStrong focus on execution, delivery, and system quality .\nAbility to translate requirements into scalable technical implementations.\nOwnership of technical outcomes without necessarily owning business definitions.\n\nWhat You’ll Own\n\nDesign and implementation of backend services and data systems.\nClickHouse-based analytics infrastructure and performance.\nData pipelines and metrics computation systems.\nEngineering quality, scalability, and reliability of systems