Senior Data Engineer

Fusion Risk Management is a fast-growing, innovative company recognized for its supportive culture and commitment to operational resilience. They are seeking a Senior Data Engineer to architect and build a new graph-backed enterprise data platform, which will integrate and unify resilience data across various systems and 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

Requirements:

• 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
• Experience with entity resolution or record-matching techniques (nice to have)
• Experience with containerized deployments (Docker, Kubernetes) (nice to have)
• 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