Ddn

Staff Software Engineer - Internal Apps

New York, New York, United States · Hybrid · FullTime

USD 150,000–185,000/year

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About the role

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EmployerDdn
LocationNew York, New York, United States
Work styleHybrid
Employment typeFullTime
Employer posted2 months ago
Salary (as stated by the employer)USD 150,000 – 185,000 per year

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

Show full description

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

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