Note: The job is a remote job and is open to candidates in USA. Fusion Risk Management is seeking a product-minded Senior Data Engineer to lead the development of a new, graph-backed enterprise data platform. This role involves architecting and owning the data platform, including designing ingestion layers, storage solutions, and APIs to unify resilience data across various environments.
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
Skills
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field
- 5+ years of experience in data engineering, backend data systems, or platform engineering roles
- Experience building or significantly expanding a data platform or data infrastructure in a production environment
- Experience working with graph, network, or highly relational data structures in a professional or academic setting
- Experience working in cloud-native environments (Azure preferred)
- Experience designing enterprise-grade integrations and connectors
- Strong SQL expertise with experience designing performant data models and production-grade transformations
- Experience with graph databases or network-oriented data problems—e.g., dependency mapping, supply chain graphs, knowledge graphs, social network analysis, or similar domains where relationships between entities are central to the data model
- Familiarity with graph query languages or traversal patterns (e.g., Gremlin, Cypher, SPARQL, or recursive SQL) and an understanding of when graph representations outperform relational models
- Experience with entity resolution, record linkage, or deduplication at scale—whether using probabilistic matching frameworks, deterministic rules, or ML-assisted approaches
- Experience building data lakes, warehouses, and distributed data systems from the ground up
- Strong understanding of ETL/ELT patterns, orchestration (e.g., Airflow, Dagster, dbt, or similar), and pipeline reliability
- Experience with open-source or self-hosted data infrastructure components and a pragmatic sense for build-vs-buy trade-offs
- Experience designing and implementing enterprise system integrations, connectors, and APIs
- Strong engineering fundamentals with focus on scalability, performance, monitoring, and security
- Familiarity with containerized deployments and orchestration (Docker, Kubernetes, Helm, or similar) (bonus)
- Experience with temporal or bitemporal data modeling patterns (bonus)
- Experience with Salesforce or ServiceNow data models and integrations (bonus)
- Strong Python or Java skills for building backend services (bonus)
- Familiarity with AI-assisted development tools (e.g., Copilot, Cursor, Claude Code, or similar) and comfort using them to accelerate engineering workflows
- Product-oriented mindset with the ability to make pragmatic architectural decisions in ambiguous, early-stage environments
- Experience with entity resolution or record-matching techniques (nice to have)
- Experience with containerized deployments (Docker, Kubernetes) (nice to have)
Benefits
- Eligible for an annual bonus
- Comprehensive benefits package including medical, dental, vision, and a 401(k) plan
Company Overview
Company H1B Sponsorship