Manager, Engineering - Runtime Team (India)

Nexla is an innovative enterprise-grade data integration platform that empowers businesses to create AI-ready data products and deliver enterprise-grade AI without coding, up to 10x faster than alternatives. Built on a Data Fabric architecture, our platform brings together multi-speed data integration, data preparation, monitoring, and governance into a unified no-code/low-code interface. Trusted by leading companies including DoorDash, LinkedIn, Johnson & Johnson, and AutoDesk, we're at the forefront of AI-powered data operations.

As Engineering Manager for the Runtime Team at Nexla, you will lead the engineering organization responsible for the core execution engines that power our data integration platform. You'll manage a team of Engineers working on distributed systems that process massive data volumes in real-time using technologies like Kafka, our proprietary execution engine, Ray for AI data processing, and other cutting-edge distributed computing frameworks. This role requires technical depth combined with strong leadership skills to guide high-performing engineers while establishing processes that enable teams to operate at maximum velocity.

• Partner with Senior/Principal Engineers to architect scalable, fault-tolerant distributed systems capable of handling enterprise-scale data workloads
• Drive technical decisions around stream processing architectures, distributed computing frameworks, and real-time data pipeline optimization
• Establish technical standards and best practices for building mission-critical, highly available distributed systems

• Manage and mentor a team of Senior and Principal Engineers, providing technical guidance while fostering professional growth and career development
• Lead hiring efforts to attract top-tier distributed systems talent and build a world-class runtime engineering team
• Conduct performance reviews, set engineering goals, and create development plans that align individual growth with business objectives
• Foster a culture of technical excellence, innovation, and collaborative problem-solving within the team

• Design and implement engineering processes that enable high-velocity development without compromising system reliability or code quality
• Establish DevOps practices, CI/CD pipelines, and deployment strategies for distributed systems operating at scale
• Implement monitoring, alerting, and observability frameworks to ensure system health and rapid incident response
• Drive adoption of agile methodologies, sprint planning, and technical project management practices optimized for distributed systems development

• Collaborate closely with Product, and AI teams to align runtime capabilities with business requirements and customer needs
• Partner with DevOps teams to ensure seamless deployment, scaling, and operational excellence of runtime systems
• Work with the Solution and Customer Engineering teams to optimize data processing workflows and integrate new AI capabilities
• Communicate technical roadmaps, system capabilities, and engineering priorities to executive leadership and stakeholders

• Oversee the operational excellence of production runtime systems, ensuring 99.9%+ uptime.
• Lead capacity planning, performance optimization, and cost management initiatives for large-scale data processing infrastructure
• Drive incident response, post-mortem analysis, and continuous improvement processes to enhance system reliability
• Ensure comprehensive disaster recovery, data consistency, and security practices across all runtime systems

• 5+ years of engineering management experience leading senior engineering teams in distributed systems or data infrastructure
• 2+ years managing Senior/Principal Engineers and technical leads in high-growth technology companies
• Proven track record of building and scaling high-performing engineering teams (10+ engineers)
• Experience establishing engineering processes, best practices, and development methodologies that drive velocity and quality

• 5+ years of hands-on engineering experience in distributed systems, stream processing, or large-scale data infrastructure
• Deep expertise with Apache Kafka ecosystem including Kafka Streams, Connect, and distributed event streaming architectures
• Experience with Ray or similar distributed computing frameworks (Spark, Flink, Dask) for AI/ML data processing workloads
• Strong background in building and operating mission-critical production systems at enterprise scale

• Expert-level understanding of distributed systems concepts: consensus algorithms, fault tolerance, partitioning, replication, and consistency models
• Extensive experience with cloud platforms (AWS, GCP, Azure) and container orchestration (Kubernetes, Docker)
• Deep knowledge of DevOps practices including Infrastructure as Code, CI/CD pipelines, monitoring, and observability tools
• Experience with database technologies, data warehouses, and real-time analytics systems

• Proven experience building systems that process terabytes of data daily with sub-second latency requirements
• Understanding of data serialization formats, compression techniques, and optimization strategies for high-throughput data pipelines
• Experience with stream processing patterns, event-driven architectures, and real-time data transformation systems
• Knowledge of data governance, security, and compliance requirements for enterprise data platforms

• Experience at high-growth startups or technology companies in data infrastructure, platform engineering, or distributed systems
• Experience with multi-tenant, enterprise-grade SaaS platforms serving Fortune 500 customers
• Experience with performance engineering, capacity planning, and cost optimization at scale
• Knowledge of data privacy regulations (GDPR, CCPA) and enterprise security frameworks

• Manage 8-12 Senior and Principal Engineers with deep expertise in distributed systems
• Collaborate with specialists in AI/ML, DevOps, Platform Engineering, and Data Engineering

• Stream Processing: Apache Kafka, Kafka Streams, Kafka Connect, custom event streaming solutions
• Distributed Computing: Ray, Apache Spark, custom proprietary execution engines
• Infrastructure: Kubernetes, Docker, AWS/GCP, Terraform, service mesh technologies
• Data Systems: Multiple database technologies, data warehouses, real-time analytics engines
• Languages: Primarily Java and Python

• Process billions of events per day with sub-second end-to-end latency
• Support enterprise customers with strict SLA requirements (99.9%+ uptime)
• Handle dynamic scaling from hundreds to millions of data transformations per minute
• Maintain data consistency and fault tolerance across geographically distributed systems

• Lead engineering for the core runtime systems that power enterprise data operations for Fortune 500 companies
• Build next-generation distributed systems that democratize access to real-time data processing and AI capabilities
• Work on cutting-edge problems in stream processing, distributed computing, and AI-powered data transformation

• Opportunity to build and scale a world-class engineering organization in a high-growth environment
• Direct impact on technical strategy and product direction at a fast-growing data infrastructure company
• Mentorship from experienced technology leaders and exposure to executive-level strategic decision making

• Collaborative, engineering-driven culture that values technical excellence and innovation
• Remote-first organization with flexible working arrangements and strong emphasis on work-life balance
• Competitive compensation package including equity, comprehensive benefits, and professional development opportunities
• Investment in cutting-edge tools, technologies, and continuous learning initiatives

Ready to lead the future of distributed data processing? Join us in building the runtime systems that power enterprise AI and data operations at global scale. Apply today to join the Nexla team!