echoloc
Get started
echoloc/ Companies/ DataNimbus

DataNimbus Tech Stack

AI-powered intelligent automation for enterprise data and payments

Software Development Dallas - Fort Worth, Texas 51–200 employees Founded 2018 Privately Held
datanimbus.com LinkedIn

DataNimbus runs a data engineering and ML platform built on Databricks, Python, and Kafka, with heavy investment in LLM solutions (Langchain, OpenAI, Hugging Face). The stack reveals dual motion: traditional data integration (MuleSoft, Informatica, Boomi) paired with modern ML infrastructure (MLflow, PyTorch, TensorFlow). Active hiring is engineering-heavy with senior-majority seniority mix, and the project list shows acute focus on productionizing customer data science workloads and LLM deployments — suggesting they're past proof-of-concept and moving toward repeatable ML ops. Current pain points cluster around compliance (SOC 2 Type II, PCI-DSS, HIPAA readiness) and production-grade ML deployment, both being addressed through active security hiring and architectural work.

Tech Stack 51 technologies

Core StackDatabricks Python Scala pandas MLflow scikit-learn TensorFlow PyTorch AWS Hugging Face Langchain OpenAI Java Kafka RabbitMQ Docker Kubernetes MuleSoft Informatica MongoDB Node.js gensim NLTK Azure GCP Spring Boot Wire Boomi SOAP Spring Framework+18 more
AdoptingSOC 2 ISO 27001

What DataNimbus Is Building

◆Challenges

  • Delivering banking projects on time
  • Large scale data migrations
  • Productionizing data science workloads
  • Building production-grade machine learning deployments
  • Soc 2 type ii readiness
  • Pci-dss readiness
  • Audit cycle management
  • Integrating legacy systems with cloud
  • Ensuring compliance with hipaa
  • Optimizing low-latency data flow

▲Active Projects

  • Datanimbus product implementations
  • Tb cash initiatives
  • Reference architecture design
  • Productionalizing customer use cases
  • 3rd party migrations
  • Productionize customer data science workloads
  • Develop llm solutions on customer data
  • Customer-facing security and trust story
  • Data classification and privacy requirements
  • Regulatory framework translation into product roadmap

Hiring Activity

Accelerating8 roles · 6 in 30d

Department

Engineering
4
Data
2
Sales
1
Security
1

Seniority

Senior
5
Mid
2
Manager
1
Company intelligence

Find more companies like DataNimbus by tech stack, pain points and active projects

Get started free

About DataNimbus

DataNimbus is a cloud-native AI and intelligent automation platform founded in 2018, headquartered in Dallas–Fort Worth. The company serves mid-market and enterprise customers needing to integrate legacy systems with cloud infrastructure while deploying machine learning and automating payments workflows. The platform stacks traditional enterprise integration tools (MuleSoft, Informatica) with modern data science frameworks (Databricks, Kafka, MLflow) to handle large-scale migrations, data science productionization, and AI-driven automation. Current operational priorities include banking project delivery, regulatory compliance (SOC 2 Type II, PCI-DSS, HIPAA), and building repeatable patterns for customer data science workload deployment.

HeadquartersDallas - Fort Worth, Texas
Company Size51–200 employees
Founded2018
Hiring MarketsIndia

Frequently Asked Questions

What tech stack does DataNimbus use?

Databricks, Python, Scala, Kafka, MLflow, TensorFlow, PyTorch, AWS, Azure, GCP, MuleSoft, Informatica, Langchain, and OpenAI. The stack combines traditional data integration tools with modern ML and LLM frameworks.

What is DataNimbus working on?

Primary focus areas: productionizing customer data science workloads, LLM solutions on customer data, large-scale data migrations, and building customer-facing security/compliance frameworks. Active projects also include reference architecture design and regulatory framework translation into product roadmap.

How this profile is built

DataNimbus'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.