Consulting Engineer

About the role

This role delivers technical advisory and implementation support for graph-powered solutions. The position guides enterprise customers through design, development, and production deployment. Success depends on hands-on expertise and translating complex concepts into actionable guidance.

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

Requirements

A Bachelor's degree in Computer Science, Information Technology, or a related field (Master's degree is a plus) is required.

Designing and developing enterprise-class applications focused on data-driven or analytics-focused systems for 3+ years demonstrates essential experience.

Data modeling and designing data structures for complex applications across relational, NoSQL, or graph databases shows necessary familiarity.

Scalable software architectures are designed and built within modern application development environments.

Data engineering experience structures and optimizes data for modern search, analytics, or AI-driven applications.

Competence in at least one of the following languages - Java, JavaScript, Python, Go, or C# - is demonstrated.

Deploying applications on modern platforms such as Linux, Docker, or Kubernetes reflects required platform skills.

Source control and development workflows using tools such as Git are used fluently.

Strong problem-solving skills enable independent and collaborative team performance.

Written and verbal communication skills clearly explain technical concepts in customer settings.

Presenting technical solutions and building strong customer relationships comes with confidence and professionalism.

Remote work in a cross-functional organization is handled effectively.

Curiosity and enthusiasm for learning new technologies and development approaches drive continuous improvement.

Client site travel is accepted as needed for engagements.

Nice to have

Professional Services experience in consultative roles for 2+ years adds depth to engagements.

Industry experience in Financial Services, Life Sciences, Manufacturing, or Technology brings relevant context.

Hands-on work with graph databases such as Neo4j, TigerGraph, JanusGraph, or similar technologies informs implementation choices.

Snowflake or Databricks, along with NoSQL/Relational providers such as MongoDB, Couchbase, or Teradata, are familiar platforms.

Data science projects involving packages such as SciLearn, Keras, or TensorFlow contribute to solution breadth.

Cloud platform experience with Amazon Web Services, Microsoft Azure, or Google Cloud Platform supports deployment scenarios.

Practical notes

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 intelligent applications by revealing relationships in data.

Graph data modeling focuses on relationships rather than isolated tables.

Modern data and AI architectures increasingly integrate graph technology for real-time insights.

Clear communication and collaboration are essential in remote and cross-functional environments.

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.