Solutions Engineer (India Startup Program)

About the role

This Solutions Engineer operates within the Neo4j Startup Program and supports customers who build on graph technologies. The role collaborates with partners to demonstrate how connected data and graph algorithms address complex problems. You will work remotely with teams across regions to validate technical use cases and guide stakeholders on product capabilities.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

What you'll do

Stakeholders refine connected data strategies, and complex requirements are translated into clear graph narratives by analyzing business needs and mapping them to graph structures. Hands-on guidance explores graph database capabilities, helping customers evaluate how Neo4j supports their data models through interactive demonstrations and scenario analysis. Solutions architects design scalable graph architectures with technical teams, aligning solutions with user success goals and business outcomes by selecting appropriate graph models and query patterns. Working prototypes of AI systems, fraud detection, and real-time recommendations are built and explained clearly to diverse audiences to validate concepts and showcase potential impact. Product value is communicated to executives, developers, and customer success teams, ensuring stakeholders understand capabilities and fit through tailored presentations and documentation.

Requirements

The posting states a bachelor's degree requirement. A degree is required for this role to ensure foundational knowledge of computer science concepts. Experience engaging with enterprise stakeholders and presenting technical concepts to non-technical audiences is necessary to convey complex ideas clearly. Comfort with remote work across time zones and collaboration through written and verbal communication is required for effective distributed teamwork. Understanding of graph concepts such as nodes, relationships, and property graphs is mandatory for modeling real-world problems accurately. Ability to articulate how graph databases support AI workflows, fraud detection, and recommendation scenarios is required to match solutions with business needs.

Practical notes

This role is part of the Neo4j Startup Program and is configured for remote work from India. Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Graph databases power applications that rely on relationships and connected data. Graph workflows often include modeling domains as nodes and edges, running path queries, and using graph algorithms. Tools such as graph databases and query languages are commonly used to explore connected data. Role holders typically work with data practitioners, product managers, and technical teams. Hands-on demonstrations and clear communication are central to success in this role.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.