Make an impact by working in sectors where technology is the enabler and innovation is the key.
At Celfocus, we make data actionable through Next‑Gen Intelligence - where data, AI and human creativity come together.
You’ll work with multidisciplinary teams and partner with leading companies to turn data into decisions, agility and long‑term value.
Explore your way in.
The opportunity:
We are seeking a highly skilled and motivated engineer to lead the design, development, and operation of a Network Digital Twin leveraging graph databases (GraphDBs). This role is central to our efforts to build a real-time, data-driven replica of our network and service topology, enabling predictive analytics, service assurance, automation, and advanced decision-making across network operations.
You will work with network architects, data engineers, and automation teams to model multi-domain topologies (physical, logical, virtual), ingest and correlate operational data, and develop scalable and query-efficient graph-based representations of the network.
Your day-to-day:
Design and implement a scalable network digital twin architecture using graph databases (e.g., AWS Neptune, Neo4j, Spanner Db).
Model complex network and service topologies, including resources, services, devices, users, and their relationships.
Develop real-time and batch ingestion pipelines from inventory systems, assurance platforms, telemetry streams, and orchestration systems.
Define and implement Cypher queries or other graph query languages to support analytics, root cause analysis, and impact assessment.
Collaborate with solution architects to ensure the digital twin supports intent-based networking, automation, and AI-driven use cases.
Integrate with external APIs (e.g., TMF APIs, SNMP, Kafka, REST) and ensure data integrity, consistency, and completeness.
Contribute to the definition of graph ontology/taxonomy, including versioning, metadata tagging, and domain-specific extensions.
Work closely with AI/ML teams to feed the digital twin into predictive models for anomaly detection, capacity forecasting, and failure prevention.
Build dashboards and visualizations to explore and interact with the digital twin.
Maintain data security, access control, and compliance with relevant standards.
What you'll bring:
Bachelor’s or Master’s degree in Computer Science, Network Engineering, Data Engineering, or related field.
3+ years experience working with GraphDBs (e.g., Neo4j, AWS Neptune) in production environments.
Knowledge of network architectures (any of IP/MPLS, SDN, 5G, transport, access, and/or cloud-native) is valued.
Proficiency in data modeling for complex systems and query languages like Cypher, Gremlin, SPARQL, etc.
Hands-on experience with network inventory systems, OSS/BSS platforms, or digital twins.
Familiarity with ETL processes, data pipelines, and integration frameworks (e.g., Kafka, NiFi, Apache Camel).
Strong programming skills in Python, Java, or Scala.
Knowledge of TM Forum Open APIs (especially TMF638, TMF640, TMF633) is a plus.
Experience with visualization tools (e.g., Neo4j Bloom, GraphXR, Gephi, or custom frontends) is also valued.
Nice to have:
Experience working in telecom or large-scale network environments.
Understanding of AI/ML integration with graph data.
Familiarity with cloud-native infrastructure and microservices (Kubernetes, Docker).
Exposure to digital twin frameworks or enterprise knowledge graphs.
Experience working in Agile/DevOps teams.
What’s in it for you:
- Hybrid way of working with flexibility and balance
- Opportunity to work on international projects
- Possibility to explore different roles and career paths
- Continuous learning and development opportunities
- A growing AI‑driven environment, working with the latest technologies
- A set of benefits and perks to support your day-to-day
A place for everyone
At Celfocus, we are committed to cultivate a diverse and inclusive workplace. As an equal-opportunity employer, we welcome applicants of all backgrounds, gender identities, and abilities.
If you require any adjustments during the selection process, please inform our Talent Acquisition Team.
