About the roleWe are seeking an experienced Senior Data Engineer to own and evolve the data architecture behind our decision intelligence platform. This is a senior software engineering role at its core, with a specialization in data: you are expected to write and ship production code in C#/.NET and operate to the same engineering standards as the rest of our platform team, while bringing deep expertise in data architecture, modeling, and performance tuning. In this role you will design and build the data schemas, pipelines, and integrations that power reporting and analytics for the customers who depend on us, while establishing the data modeling standards, governance practices, and performance budgets that scale across our engineering organization. The successful candidate will possess deep, hands-on experience with cloud-native data engineering on Microsoft Azure and Snowflake, strong SQL and data modeling skills, proven ability to deliver high-quality cloud-native services in C#/.NET, and a passion for building reliable, well-governed data systems.
What you'll do- Own and evolve the data architecture for our Azure-based SaaS platform as data collection and reporting needs grow
- Design, build, and maintain data schemas, pipelines and transformation processes feeding data lakes and data warehouses (e.g. Snowflake)
- Develop and optimize reporting and analytics experiences delivered through embedded analytics platforms (e.g. Sisense)
- Profile and tune query performance across the platform (Snowflake and operational data stores), establishing performance budgets and monitoring
- Design and implement integrations with customer and enterprise data sources (APIs, event streams, batch interfaces)
- Build and extend scalable, secure data and platform services in C#/.NET, contributing production code to containerized services alongside the broader engineering team
- Lead by example on code quality and automated test coverage, provide constructive feedback on pull requests, and participate in Agile ceremonies including sprint planning, stand-ups, refinements, and retrospectives
- Define data modeling standards, governance practices, and documentation for the engineering organization and mentor other engineers to support adoption
- Ensure data architecture and pipelines meet data privacy and compliance requirements (GDPR, HIPAA, SOC 2), including data residency, retention, and access controls
What we're looking for- Required
- 5+ years of professional data engineering experience, with at least 2 years in a senior or lead capacity
- Deep, hands-on experience with Snowflake: warehouse design, performance tuning, cost management, and data modeling
- Strong SQL skills, including query profiling and optimization at scale
- Hands-on experience building data pipelines and integrations on Microsoft Azure (Data Factory, Event Hubs, Functions, or similar Azure services)
- Working understanding of data privacy and compliance frameworks (GDPR, HIPAA, SOC 2) and how they shape data architecture decisions such as residency, retention, encryption, and access control
- Experience designing integrations with external enterprise data sources (REST APIs, change logs, file-based, streaming events)
- Understanding of data pipeline requirements and workflows needed to support machine learning capabilities within a platform (model training data sets, RAG data stores, etc.)
- Track record of owning data architecture decisions and communicating them to technical and non-technical stakeholders
- Experience leveraging agentic tools and workflows to aid research, prototyping, design and implementation tasks while ensure quality standards are maintained
- Hands-on experience building and shipping production services in .NET C#, including REST/GraphQL/gRPC APIs and containerized (Docker) deployment on Azure
- Strong understanding of software architecture and design patterns, Azure cloud-native methodologies (microservices, containerization and orchestration, DevOps and CI/CD, Infrastructure as Code), and S.O.L.I.D. principles, consistently producing modular, testable code
- Strong written and verbal communication skills and the ability to work effectively in a team-oriented, collaborative environment, including Agile delivery practices
- Preferred
- Experience with Sisense or comparable embedded analytics platforms
- Experience with multi-tenant SaaS data architectures and tenant data isolation
- Experience building products supporting both SaaS and on-premise deployments
- Experience with Python (or equivalent) for lightweight data science tasks/projects
What we offer- Competitive compensation and benefits.
- Flexible work environment.
- The opportunity to build and shape a premium support function with measurable customer impact.
- A collaborative culture with close partnership across Support, Engineering, Product, and Customer Success.
- Professional growth within a scaling SaaS organization.
The pay range for this role is:
140,000 - 170,000 USD per year (InRule Chicago)
