Technical Lead

Technical Lead - Nova

About Vymo

Vymo transforms how financial institutions manage their distribution networks and collections operations. Our AI-powered DMS and CMS platform serves 350,000+ users across 70+ global financial enterprises, enabling banks and insurers to optimize performance across their distribution channels, and empowering banks to drive operational excellence in collections.

Why Global Leaders Choose Vymo

Vymo partners with financial institutions through a comprehensive solution that combines deep domain expertise with cutting-edge technology. Our platform's human-centric design, coupled with enterprise-grade scalability and AI-powered intelligence, delivers measurable business outcomes while ensuring high user adoption and engagement.

Recognition & Trust

Backed by $45M+ funding from Peak XV partners (formerly Sequoia), Emergence Capital, and Bertelsmann Investments.

Trusted by leading global financial institutions including Berkshire Hathaway, AIA, AXA, SBI Life, HDFC Bank, Aditya Birla Capital, Tata AIA, ABSLI and many more!

Recognized by Celent in their 2024 Distribution Management Solutions report, with an Advanced Functionality ranking.

Featured in Gartner's Market Guide for Sales Engagement Applications and recognize

d as a"Strong Performer" in Forrester's Sales Engagement Wave Report.

Microsoft 'AI for All' award winner for innovation in artificial intelligence.

Explore Vymo on https://vymo.com/careers/

About the Role

We are looking for a Technical Lead to join team Nova to drive the architecture and development of both backend systems and data platforms at Vymo. 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.

About Nova

Team 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:

• Real-time and batch data pipelines, Building resilient ingestion and ETL pipelines
• Designing efficient OLAP schemas and large-scale analytical workloads in ClickHouse
• Large-scale data transfer and synchronization between PostgreSQL and ClickHouse
• ClickHouse data modeling and query optimization
• Improving performance, reliability, and scalability of data systems
• Metrics platforms, dashboards, and analytical systems
• Java-based microservices, distributed backend systems that power key metrics and data access
• High-volume event processing and operational observability

Our 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.

Key Responsibilities

Backend & Platform Engineering

• Design and implement scalable microservices using Java (Spring Boot) / Node.js.
• Build high-performance APIs and distributed systems.
• Implement caching strategies (Redis/Memcached) to optimize latency and throughput.
• Ensure system reliability, scalability, and maintainability in production.

Data Engineering & Analytics Infrastructure

• Design and implement data pipelines (batch & streaming) based on product and analytics requirements.
• Build and optimize ClickHouse schemas and queries for large-scale analytics workloads:
• Efficient ingestion pipelines
• Partitioning and indexing strategies
• Query performance tuning
• Implement data transformations and aggregation layers for downstream consumption.

Metrics & Data Systems

• Implement systems for metrics computation and aggregation (real-time or batch).
• Ensure correctness, consistency, and efficiency of data processing pipelines.
• Optimize storage and retrieval for high-volume event data.

Dashboards & Data Access

• Build and maintain Superset dashboards and underlying datasets.
• Enable efficient querying and visualization by structuring clean, performant data layers.
• Work with stakeholders to implement required reporting and analytics use cases.

System Design & Performance

• Lead technical design and architecture discussions.
• Identify bottlenecks and optimize across:
• Backend services
• Databases (OLTP & OLAP)
• Data pipelines
• Ensure systems are designed for scale, fault tolerance, and observability.

Leadership & Execution

• Lead and mentor a team of engineers; drive execution and delivery.
• Perform code reviews and enforce engineering best practices.
• Break down complex problems into actionable engineering tasks.
• Collaborate with product and analytics teams to implement requirements effectively.

Required Skills & Experience

• 610+ years of experience in backend and/or data engineering.
• Strong expertise in:
• Java (Spring Boot, Microservices)
• Node.js (Express/NestJS or similar)
• Hands-on experience with ClickHouse (or similar OLAP systems).
• Hands-on experience with Postgres (or similar OLTP systems).
• Experience in building data pipelines and processing systems.
• Strong understanding of distributed systems and system design.
• Experience with:
• Caching (Redis, etc.)
• Message queues (Kafka/RabbitMQ)
• SQL and query optimization
• Experience with Superset or similar BI tools.

Leadership Expectations

• Proven ability to lead small-to-mid sized engineering teams.
• Strong focus on execution, delivery, and system quality.
• Ability to translate requirements into scalable technical implementations.
• Ownership of technical outcomes without necessarily owning business definitions.

What Youll Own

• Design and implementation of backend services and data systems.
• ClickHouse-based analytics infrastructure and performance.
• Data pipelines and metrics computation systems.
• Engineering quality, scalability, and reliability of systems