Datarails Tech Stack

FP&A platform bridging Excel and automated financial workflows

Software Development New York 201–500 employees Privately Held

Datarails operates at the intersection of legacy finance (Excel-dependent workflows) and modern data infrastructure. The stack reveals a data-platform orientation—Python, Airflow, Dagster, dbt, Kubernetes—paired with LLM adoption across OpenAI, Anthropic, Gemini, Mistral, and Groq. The project list centers on customer success and AI-workflow adoption, while pain points expose friction: manual data consolidation and slow Excel-to-automation migration. Hiring velocity is accelerating across support and sales, indicating product-market fit pressure and customer onboarding as bottlenecks.

Tech Stack 35 technologies

Core StackPower Query Salesforce Python Apache Airflow Dagster Celery Temporal FastAPI Django Kubernetes Node.js TypeScript OpenAI Anthropic Angular Azure AD Okta Intune Confluence dbt Zendesk Jira Terraform Helm Gong Google Gemini Mistral Groq Notion macOS+2 more

What Datarails Is Building

◆Challenges

Reliance on excel spreadsheets
Retention and expansion
Adoption of ai-enabled workflows
Customer shift toward ai-powered finance
Reducing data consolidation complexity
Manual data consolidation
Inefficient reporting
Data consolidation inefficiencies

▲Active Projects

Global customer success strategy
Customer journey frameworks
Ai-enabled workflows adoption
Data integration into product
Customizing datarails platform
Training customers on product updates
Client datarails implementation
Process optimization for financial reporting
Implement add-on modules
Drive adoption of new products

Hiring Activity

Accelerating5 roles · 5 in 30d

Department

Finance

Support

Sales

Seniority

Mid

Junior

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About Datarails

Datarails is a financial planning and analysis platform serving finance teams at mid-market companies. The product automates financial reporting and forecasting while preserving Excel as the native modeling interface—a design choice that bridges operational familiarity with programmatic scale. The core value proposition addresses a persistent pain point: reducing time spent on manual data consolidation and spreadsheet wrangling, freeing capacity for strategic analysis. The engineering stack (Airflow, dbt, FastAPI, Kubernetes) supports data-pipeline automation; LLM integrations signal movement toward AI-assisted financial workflows. Based in New York with 201–500 employees, the company is actively hiring across customer success, support, and sales.

HeadquartersNew York

Company Size201–500 employees

Hiring MarketsUnited States

Frequently Asked Questions

What tech stack does Datarails use?

Datarails uses Python, Apache Airflow, Dagster, and dbt for data workflows; FastAPI and Django for backend services; Kubernetes for orchestration; and integrates OpenAI, Anthropic, Google Gemini, Mistral, and Groq for LLM features. Salesforce and Power Query handle finance data ingestion.

Where is Datarails headquartered?

Datarails is headquartered in New York and employs 201–500 people. Current hiring is focused in the United States.

How this profile is built

Datarails's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.