Job description
SqlDBM
SqlDBM is the leading cloud-native data modeling platform trusted by Fortune 500 enterprises across financial services, healthcare, retail, and technology. We help data teams design, govern, and evolve their data architecture with confidence — at scale, in real time, collaboratively.
We are a profitable, growing company with enterprise customers including some of the world's largest organizations. Our platform is in the middle of a significant transformation: we are embedding AI deeply into every layer of the data modeling workflow, and we are looking for engineers who want to build what comes next.
About the role
This is not a role where you will bolt AI onto an existing product as an afterthought. SqlDBM is rebuilding core workflows around AI — from how data architects design schemas to how engineers validate changes to how governance teams maintain compliance across enterprise environments.
You will work at the intersection of .NET backend engineering and applied AI — building the systems that make AI a first-class, reliable, enterprise-grade capability inside one of the most technically demanding categories in data infrastructure.
This role sits inside our dedicated AI group and reports directly to senior leadership. You will have a short line to product, architecture, and business decisions — no layers, no queue. The team is small by design, moves fast, and has direct visibility into enterprise customer needs.
What makes this role different
You are not building a chatbot. You are building AI infrastructure for enterprise data teams — systems that understand schema context, generate governed artifacts, and integrate into the workflows of the world's most sophisticated engineering organizations. The problems are hard. The customers are demanding. The work is meaningful.
What you'll do
You will own and extend the backend systems that power SqlDBM's AI capabilities. Specific areas include:
AI-assisted data modeling workflows — backend services that allow users to describe their data needs in natural language and receive accurate, governed schema output
Context-aware intelligence — systems that use the full richness of a user's data model, naming standards, and governance rules to produce AI output that is specific to their environment, not generic
Automated documentation and metadata generation — AI pipelines that analyze existing schemas and produce accurate, consistent business documentation at scale
Integration with enterprise AI ecosystems — API layers that allow external AI agents and tools to call SqlDBM as a trusted source of schema context
Consumption tracking and orchestration — backend infrastructure that manages AI request routing, model selection, cost optimization, and usage metering
Governance-aware AI workflows — systems that embed approval, validation, and compliance logic into AI-generated outputs before they reach production
MCP server development — building and extending SqlDBM's Model Context Protocol server so that external AI agents and LLM-based tools can use SqlDBM as a trusted, real-time schema authority
Tech Stack includes:
C# / .NET
AWS
PostgreSQL
Redis
REST APIs
LLM Integration
MCP
Microservices
Our backend is built on modern .NET with a microservices architecture deployed on AWS. AI capabilities are built on top of major LLM providers via API, with proprietary prompt engineering, context management, and output validation layers that are core intellectual property of the platform.
Qualifications
5+ years of backend engineering experience with C# and .NET
Strong understanding of REST API design and asynchronous service architectures
Experience integrating with external APIs and managing complex data pipelines
Comfort working with LLMs via API — understanding of prompt construction, context management, token economics, and output validation
Experience building systems that handle variable, structured data — schemas, metadata, or similar
Strong engineering fundamentals — testing, code review, system design, observability
Ability to work independently in a remote-first, fast-moving engineering team
Strong Plus
Experience with data modeling, database design, or data engineering tooling
Familiarity with enterprise data platforms — Snowflake, Databricks, dbt, or similar
Background building developer tools or platforms used by technical teams
Experience with agentic AI workflows, tool-use patterns, or AI infrastructure
Knowledge of semantic layer concepts, ontologies, or structured metadata systems
Familiarity with Model Context Protocol (MCP) — building or consuming MCP servers in agentic AI architectures
What We Offer
Competitive base salary and equity in a profitable, growing company
Fully remote — work from anywhere
Direct impact on product direction — small team, no layers, your work ships to enterprise customers
Work on genuinely hard technical problems at the frontier of AI and enterprise data infrastructure
Collaborative, engineering-driven culture that moves fast and trusts its people
Benefits - comprehensive insurance coverage for employees and their dependents — including medical, dental, vision, life, and both short- and long-term disability, parental leave, an employer-sponsored 401(k) retirement plan, and stock options.
Why now
SqlDBM is at an inflection point. We have enterprise customers, a profitable business, and a product that is becoming something significantly more powerful. The engineers who join now will shape what that means and build the systems that define the next chapter of data architecture tooling.
Originally posted on Himalayas
Who can apply
Eligible countries: United States. Accepted UTC offsets: UTC-10, UTC-9, UTC-8, UTC-7, UTC-6, UTC-5, UTC+14. Review the full description for employer-specific work authorization, residency and schedule requirements.