Senior Data Engineer
Fusion Risk Management
Data Science
United States · Remote
The Role
We’re looking for a product-minded Senior Data Engineer to lead the buildout of a new, graph-backed enterprise data platform at Fusion.
This is not a maintenance role. You will architect and own a new data platform from the ground up—designing the ingestion layer, graph and relational storage, entity resolution pipelines, and APIs that unify resilience data across customers, systems, and cloud environments.
You will define how data is ingested, resolved, modeled as a graph, governed, and exposed across Fusion’s ecosystem. This platform will power dependency analysis, recovery modeling, predictive intelligence, and a new generation of resilience products.
This is a high-ownership opportunity for someone who wants to build something foundational, work with graph and network data structures at scale, and create a platform that becomes core to Fusion’s long-term strategy.
Key Responsibilities
• Architect and build Fusion’s next-generation data platform from the ground up, including a graph database layer, relational storage, and data lake components.
• Design and implement scalable ETL/ELT pipelines to ingest and transform data from customer environments, internal systems, and third-party platforms using managed connector frameworks.
• Build and maintain entity resolution pipelines that match, merge, and link records across disparate sources into a unified graph model.
• Design and implement graph data models that represent operational dependencies, recovery sequences, and organizational relationships—supporting traversal queries across complex, multi-hop networks.
• Develop temporal and bitemporal data models that capture how entities and relationships change over time, enabling historical replay and audit-grade versioning.
• Establish best practices for data governance, quality, observability, lineage, and security across the platform.
• Build backend services and APIs that expose graph queries, entity lookups, and data capabilities to downstream applications and ML systems.
• Support containerized deployment across both managed cloud and customer-hosted (reverse SaaS) environments.
• Partner with product and engineering leadership to shape the long-term data platform roadmap.