Citta
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About
Citta is a unified data analytics platform designed to bridge the gap between complex data engineering and operational business applications. The platform enables low-code developers, ETL engineers, and ML teams to collaborate on transforming raw data pipelines into governed, reusable engines. By appifying these pipelines, organizations can move away from fragile custom scripts and siloed batch jobs toward structured, productized solutions that power critical business processes. The primary goal is to provide a product layer for data logic, ensuring that core functions like incentive engines or fraud detection are managed with the same rigor as external software products. The platform functions by offering a suite of tools for building data projects that include ingestion, quality checks, machine learning, and insight generation. A standout feature is the support for Apps on a Continuous Timeline, which allows users to build data objects with time-travel capabilities to model complex historical scenarios. Citta also simplifies the deployment lifecycle with a ground-up DevOps approach, featuring one-click environment creation, release management, and deployment. This allows teams to iterate quickly without the heavy platform tax usually associated with scaling internal data systems. Citta is specifically tailored for large enterprises and solution providers who need to scale their internal systems without relying on fragmented documentation or manual coordination. It is ideal for industries managing high-stakes logic, such as finance for fraud detection or retail for pricing engines. For consulting firms, the platform offers a way to productize their AI and data offerings, reducing the engineering overhead required to build and maintain custom client solutions. This creates an internal marketplace of reusable assets that can be distributed across various business units or clients. What differentiates Citta from standard ETL or BI tools is its focus on productization and appification. While traditional tools focus on moving or visualizing data, Citta focuses on the logic and governance of the operational processes themselves. It provides a unified environment where data engineering, AI/ML Ops, and BI are merged to eliminate management silos. With a robust catalog of connectors for systems like SAP HANA, Oracle EBS, and Snowflake, Citta integrates deeply into existing enterprise ecosystems while providing a multicloud orchestration layer to manage pipelines across different cloud platforms.
Pros & Cons
Supports complex time-travel data modeling for simulating historical scenarios.
Eliminates siloed management by unifying Data Engineering, BI, and ML Ops.
Offers a wide range of enterprise connectors including SAP HANA and Oracle EBS.
Enables one-click deployment of production-ready data applications.
Specific pricing tiers and monthly costs are not publicly disclosed on the website.
The full list of upcoming connectors is not yet available for production use.
Use Cases
Enterprise Data Engineers can transform fragmented Python scripts into governed, reusable pipeline apps for various business units.
ML Engineers can integrate machine learning models into operational workflows with automated quality checks and insight generation.
Consulting Firms can productize their proprietary data logic and AI offerings into an internal marketplace for faster client delivery.
Business Users can interact with knobs on operational apps to self-serve changes to business rules and simulation scenarios.
Platform
Task
Features
• unified data pipelines
• multicloud orchestration
• low-code app builder
• ai/ml ops integration
• erp connector catalog
• internal marketplace
• one-click devops
• time-travel data objects
FAQs
What types of data sources can Citta connect to?
Citta supports a wide range of connectors including JDBC databases like PostgreSQL and Snowflake, ERP systems such as SAP HANA and Oracle EBS, and various cloud storage and Hadoop files.
How does Citta handle deployment and DevOps?
The platform features one-click deployment for environments and releases, designed from the ground up to simplify moving productized solutions into production.
What is the benefit of appifying a data pipeline?
Appifying turns custom scripts into governed, reusable engines that business users can interact with through operational apps, reducing reliance on manual coordination and shadow IT.
Does the platform support machine learning workflows?
Yes, Citta unifies Data Engineering and AI/ML Ops, allowing teams to build pipelines that include machine learning models and business rules for insight generation.
Can I manage pipelines across different cloud providers?
Yes, Citta provides multicloud orchestration capabilities, enabling users to manage and run their data pipelines across multiple cloud data platforms.
Pricing Plans
Enterprise
Unknown Price• Unified data pipelines
• Time-travel data objects
• Multicloud orchestration
• One-click DevOps
• Internal solution marketplace
• Custom ERP connectors
Job Opportunities
Full Stack Senior Software Engineer
Transform fragmented data pipelines into governed, productized operational apps to streamline business logic like fraud detection and pricing for enterprise teams.
Education Requirements:
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
Experience Requirements:
4–7 years of professional full-stack development experience.
Proficiency in JavaScript (ES6+), HTML5, CSS3, and modern frontend frameworks such as React, Angular, or Vue.js.
Strong backend expertise in Java, Spring Boot, or equivalent frameworks for building RESTful services.
Hands-on experience with PostgreSQL (or other relational databases) — including schema design and performance tuning.
Solid grasp of data structures, algorithms, and system design principles.
Other Requirements:
Experience working in agile development environments with version control (Git).
Experience developing and scaling SaaS or cloud-native applications (AWS, GCP, or Azure).
Familiarity with CI/CD pipelines, containerization (Docker, Kubernetes), and DevOps workflows.
Exposure to data engineering, analytics, or AI-driven products.
Prior experience in startup or fast-paced product teams with end-to-end feature ownership.
Responsibilities:
Design and develop scalable, maintainable, and high-performance web applications across the stack.
Participate and contribute to Architecture and Design discussions to make key technical decisions.
Build and optimize RESTful APIs, microservices, and database models supporting complex data workflows.
Design efficient PostgreSQL schemas, ensuring data integrity, scalability, and performance.
Mentor and guide junior developers, conduct code reviews, and champion engineering best practices.
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Full-Stack Software Developer
Transform fragmented data pipelines into governed, productized operational apps to streamline business logic like fraud detection and pricing for enterprise teams.
Experience Requirements:
3+ years of professional software development experience.
Strong proficiency in React.js and modern JavaScript/TypeScript.
Experience with backend technologies (Java, Node.js, Python, or similar).
Experience with Data Engineering and AI tools
Solid understanding of REST APIs and/or GraphQL.
Other Requirements:
Familiarity with cloud platforms (AWS, GCP, or Azure).
Knowledge of databases (SQL and/or NoSQL).
Strong problem-solving and debugging skills.
Excellent communication and collaboration abilities.
Experience building or scaling SaaS products.
Responsibilities:
Develop and maintain full-stack applications with a focus on frontend React development.
Collaborate with product managers and designers to create user-friendly interfaces for complex data applications.
Design, build, and scale backend services and APIs that support a SaaS environment.
Ensure high performance, security, and reliability of the application.
Contribute to architectural decisions and participate in code reviews.
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