The role
From DDN's own posting.
We’re looking for a Senior Software Engineer to build internal applications on top of DDN’s enterprise data platform. This is a largely greenfield charter — a new function dedicated to full-stack tools and AI-powered services for GTM, Finance, Support, and Product. We are building applications that surface data for decision-making and applications that improve and automate the operational processes that run the business. You’ll have early prototypes to learn from, but the mandate is to define this product portfolio and build it out. Data and analytics engineers own what’s underneath; the applications themselves — frontend, backend, deployment, model integration — are yours.
What You’ll Own
Internal applications — design, build, and operate full-stack web apps (FastAPI/Flask + React/TypeScript today, but technology choices are open) that put data and AI into stakeholders’ hands — both as decision-support interfaces and as purpose-built tools that let them do operational work
AI/LLM integration — build features powered by LLMs and ML — classification, extraction, summarization, copilots, agentic workflows — choosing whichever models, providers, and frameworks fit the problem
Application infrastructure — deploy and operate apps on GCP (App Engine, Cloud Run, GKE), connect them to the data platform, manage auth, own CI/CD and app security
Product surface — define what good looks like for this new function: which problems are worth a custom app vs. a BI dashboard, what our reusable building blocks should be, and how we ship reliable, observable services people depend on
Collaboration — partner with stakeholders to scope the right tool for the job, with analytics engineers to shape the underlying data models, and with data engineers on platform constraints
Your Experience Includes
5+ years building production software, with meaningful time spent on full-stack web applications
Strong Python — APIs (FastAPI, Flask, or similar), data access patterns, packaging, testing
TypeScript/React (or comparable framework), component design, interactive data UIs
Hands-on experience with GCP application services — App Engine, Cloud Run, GKE, IAM
Strong SQL and comfort working with cloud data warehouses (BigQuery in our case) — you can write a query, understand its cost, and design an app’s data access layer around it
Experience developing and deploying AI/LLM-powered applications in production — prompt design, structured output, evaluation, cost/latency tradeoffs, awareness that the model and tooling landscape changes quickly
Experience operating what you ship — logging, monitoring, error handling, debugging in production
Experience with software engineering best practices: CI/CD, automated testing, observability, secure application design
Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
Nice to Have
Experience building AI-native applications such as text-to-SQL interfaces, copilots, agentic workflows, or automated insight-generation systems
Hands-on experience with one or more LLM provider APIs (Anthropic’s Claude, OpenAI, Google, open-weight models, etc.) and agent frameworks (Claude Agent SDK, LangGraph, or similar)
Experience with managed AI/ML platforms (Vertex AI, SageMaker, or similar) — model serving, embeddings, evaluation tooling
Familiarity with dbt and modern data warehouse patterns from a consumer’s perspective
Experience with Airflow for triggered jobs and background work
Familiarity with Terraform for managing application infrastructure
Background designing data-heavy UIs — tables, drill-downs, large result sets, interactive exploration
Prior experience as the first or only application engineer on a data team — comfort owning the full lifecycle