Key Responsibilities:

- Lead, mentor, and grow a team of backend and frontend engineers (610 members).

- Foster a culture of ownership, high performance, and continuous improvement.

- Drive sprint predictability, engineering quality, and delivery excellence.

Technical Strategy & Architecture:

- Define and evolve the architecture for scalable, high-performance search systems.

- Drive design decisions across indexing pipelines, query processing, and ranking frameworks.

- Ensure systems are resilient, observable, and cost-efficient.

- Guide adoption of modern search paradigms including semantic search and vector-based retrieval.

Product & Search Experience Ownership:

- Own key search metrics: relevance, latency, CTR, and conversion.

- Collaborate with Product, UX, and Data teams to define and execute the search roadmap.

- Translate user behavior and analytics into actionable improvements in discovery experience.

- Champion experimentation culture (A/B testing, offline evaluation frameworks).

Delivery & Execution:

- Oversee end-to-end delivery of search platform enhancementsfrom design to production.

- Ensure high-quality releases, minimizing gaps between product requirements and engineering output.

- Manage dependencies across teams and proactively mitigate risks.

- Drive continuous improvements in development processes, CI/CD, and observability.

Stakeholder Management:

- Communicate effectively with product, business, and senior leadership.

- Provide clarity on trade-offs, timelines, and technical risks.

- Partner with analytics and marketplace teams to align on business outcomes.

Must Have:

- 9-11 years of software engineering experience with 2-4 years in engineering management.

- Strong experience with search and retrieval systems (e.g., Elasticsearch/OpenSearch).

- Proven track record in eCommerce or large-scale product discovery platforms.

- Hands-on experience with Java or Python and modern backend architectures.

- Deep understanding of search relevance tuning, indexing pipelines, and query optimization.

- Experience with Agile execution, sprint predictability, and engineering metrics.

- Strong system design skills for high-scale, low-latency systems.

- Experience with cloud platforms (AWS/GCP), Kubernetes, and CI/CD pipelines.

Good to Have:

- Exposure to vector search, embeddings, or LLM-powered discovery.

- Experience with personalization or recommendation systems.

- Prior experience scaling or migrating search infrastructure.

Success Metrics:

- Measurable improvement in search relevance (CTR, conversion rate, engagement).

- Reduction in search latency and system bottlenecks.

- Improved team velocity, predictability, and retention.

- Establishment of a robust experimentation and metrics-driven culture.