Senior Data Engineer

Senior Data Engineer (162397) Chicago, Illinois

Salary: USD
135000 - USD
155000 per year

We’re looking for a product minded Senior Data Engineer to lead the buildout of a new, graph backed enterprise data platform at Client.

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 Client’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 Client’s long-term strategy.

• Architect and build Client’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.

• 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 Service Now 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.