Senior Software Engineer (Graph Technologies)

Senior Software Engineer (Graph Technologies) at Ardoq Help organizations untangle complexity by building the AI-powered digital blueprint of the modern enterprise. The Ardoq Story Ardoq is one of Norway’s most exciting scale-ups—a truly global company built out of Oslo. We are a SaaS platform that helps organizations understand, manage, and evolve their complex digital landscape. By providing a dynamic, collaborative "digital twin" of their business, we connect systems, people, and processes to drive better decision-making and accelerate digital transformation. Today, we help some of the world’s most complex organizations (like ExxonMobil and British Telecom) gain clarity in a changing world. We are backed by leading global tech investors, including EQT and One Peak, giving us the stability of a mature company with the speed and soul of a startup. About the Role In an AI-fueled world, enterprises face a new kind of complexity—not just more data, but more noise. As a Senior Engineer in the Graph team, you will be at the heart of our Knowledge Graph and AI-driven transformation, building the tools that turn fragmented inputs into connected, contextual intelligence. Ardoq has been developing its own graph database engine. A unique multi-model graph database with support for point in time queries, zero cost branching, semantic models and reasoning. This technology is essential for achieving the strategic goal of developing enterprise digital twins. At Ardoq, we value autonomy and psychological safety. In this role, you will have the influence to shape the domain and the ownership to see your ideas through to execution. What you’ll do Build the engine: Extend GraphLake's storage layer, query planner and execution engine. Own changes end-to-end — data structure choice, on-disk format, cardinality estimation, traversal operators, production rollout. Work in the OneGraph model where RDF triples and labelled property graphs share storage and execution. Make temporal and branched graphs cheap at scale: Push our point-in-time and zero-cost branching primitives — built on immutable, append-only storage with structural sharing — to the largest customer graphs without sacrificing read latency or write throughput. Strengthen the semantic reasoning layer: Improve how the engine handles OWL ontologies, SPARQL query planning, SHACL validation, and rule-based inference. Harden it for production: Profile, benchmark and tune the hot path. Reduce tail latency, eliminate lock contention, improve memory locality, and pay down architectural debt before it becomes a customer incident. Build the test harness that catches regressions before they ship. On a typical day, you will You'll start by digging into a benchmark regression, a SPARQL planner edge case, or a concurrency bug surfaced overnight by load tests. You'll spend hours in the engine code — likely on meta-data files,, the persistence layer, or the inference loop — pair-debugging at the data structure level, reading flamegraphs, and reasoning about correctness under concurrent writes and time-travel reads. You'll review a colleague's design for a new traversal operator or a delta-layer encoding with the same rigour you'd want on your own. In the afternoon, you'll trade notes with Product on which semantic primitives the AI layer needs to expose, then ship the change behind a feature flag with a benchmark and a correctness proof that holds up. Who you are We are looking for a collaborator who balances deep domain expertise with a humble, "team-first" mindset. The Experience: You have built non-trivial systems software that ran in production — a graph database, RDF triple store, query engine, distributed database, or comparable infrastructure. You've worked on or with systems such as Neo4j, Memgraph, TigerGraph, JanusGraph, Dgraph, Stardog, RDFox, GraphDB, AllegroGraph, Virtuoso, Apache Jena, TerminusDB, Datomic, or XTDB — and you can talk credibly about the design trade-offs each one made. The S