Datarails is a cloud-based financial planning and analysis (FP&A) platform designed for small and medium-sized businesses (SMBs). It automates manual monthly and quarterly reporting, connects to systems like ERP, CRM, HRIS, and Excel files, and consolidates financial data into a single cloud-based platform. Datarails automates processes such as FX conversions, eliminations, hierarchies, and financial adjustments, while allowing users to continue working in Excel. Note: Datarails is primarily designed for finance teams and may not be suitable for organizations seeking a non-Excel-based workflow. Source
Who is the target audience for Datarails?
Datarails is built for CFOs, FP&A professionals, controllers, finance managers, and finance teams in public companies, pre-IPO organizations, lower enterprise companies, and small to mid-sized businesses. It serves industries such as construction, financial services, logistics, manufacturing, healthcare, technology, and more. Note: Organizations outside these roles or industries may find limited applicability. Source
Role-Specific Information: Senior Software Engineer, AI Builder (Python)
What are the main responsibilities of the Senior Software Engineer, AI Builder (Python) role at Datarails?
The Senior Software Engineer, AI Builder (Python) is responsible for end-to-end ownership of product features, from requirements and technical design through implementation, testing, deployment, and production monitoring. The role involves using AI coding agents as a core part of the engineering workflow, making independent technical decisions, and collaborating closely with Product to translate business problems into technical solutions. Note: This role requires accountability for the quality, security, performance, and maintainability of both human- and AI-generated code. Source
What qualifications and experience are required for the Senior Software Engineer, AI Builder (Python) position?
Required qualifications include a BSc in Computer Science or equivalent experience, 7+ years of experience building and shipping production software, strong system design fundamentals, and experience building scalable SaaS systems in the cloud. The role also requires hands-on experience with AI coding tools (such as Claude Code, Cursor, Codex, or GitHub Copilot), strong product mindset, and excellent communication skills in English. Note: Experience with modern frontend technologies, CI/CD pipelines, and cloud infrastructure is considered a plus but not mandatory. Source
What makes the Senior Software Engineer, AI Builder (Python) role at Datarails different from traditional engineering roles?
This role emphasizes end-to-end ownership, AI-native development (using AI coding agents for development, testing, debugging, and automation), and direct partnership with Product. Engineers are expected to review and validate AI-generated code, make independent architectural decisions, and contribute across backend, frontend, QA, CI/CD, and infrastructure as needed. Success is measured by the quality and impact of shipped features, not by code volume. Note: Candidates seeking narrowly defined backend roles may find this position broader than expected. Source
Features & Capabilities
What are the key features and benefits of Datarails?
Datarails automates up to 75% of manual spreadsheet tasks, consolidates financial data from over 600 systems, provides real-time dashboards and AI-powered analytics, and allows users to work directly in Excel. It supports unlimited data dimensions, offers quick implementation (most setups in 4-6 weeks), and includes white-glove support with a dedicated Customer Success Manager. Note: Datarails is best suited for organizations comfortable with Excel-based workflows; teams seeking a non-Excel interface may want to consider alternatives. Source
How does Datarails integrate with other systems?
Datarails connects to over 600 systems, including ERP, CRM, HRIS, and Excel files, consolidating data into a single cloud-based platform. For a full list of integrations, visit the Datarails Integrations page. Note: Integration with highly specialized or custom systems may require additional configuration. Source
Security & Compliance
What security and compliance certifications does Datarails have?
Datarails is certified for SOC 2 Type II, GDPR compliance, and ISO 27001. The platform features role-based permissions, full audit trails, compliance tracking, and data encryption. For more details, visit the Datarails Trust Center. Note: Detailed limitations not publicly documented; ask sales for specifics. Source
Implementation & Support
How long does it take to implement Datarails, and what support is provided?
Most Datarails implementations are completed within 4-6 weeks, with some modules (such as financial statements or cash management) implemented in as little as 1-2 weeks. Customers receive hands-on, daily live assistance and a dedicated Customer Success Manager with a finance background at no additional cost. Training resources include self-paced modules, live sessions, webinars, and certification programs via Datarails University and Academy. Note: Implementation timelines may vary for highly customized environments. Source
Customer Success & Case Studies
Can you share specific case studies or success stories of Datarails customers?
Yes. For example, NovaTech saved hundreds of thousands of dollars and four weeks a year using Datarails (case study), Great Falls Clinic reduced financial reporting time from days to minutes (case study), and Avenue One regained 40 hours of work time (case study). For more, see the Datarails success stories page. Note: Results may vary depending on company size and complexity.
Career Opportunities at Datarails
Where can I find information about careers at Datarails?
Information about open positions and working at Datarails is available on the Datarails careers page. The page highlights opportunities for career growth and impact, and lists available teams such as CS – Live, CS – Onboarding, Marketing, Partnerships, Product, RnD, and Sales – AE. Note: Some positions may be location-specific. Source
Technical Documentation & Resources
Where can I find technical documentation about Datarails integrations and formulas?
Technical documentation for Datarails integrations is available on the Integrations page. Documentation for Datarails formulas can be found at the Datarails formulas documentation. For information about the mobile app, visit the Datarails Mobile App page. Note: Some advanced documentation may require login or customer status. Source
Datarails is a cloud-based financial planning and analysis (FP&A) platform designed for SMBs, transforming manual monthly and quarterly reporting into automated, scalable processes.
Our solution connects to organizational systems such as ERP, CRM, HRIS, and complex Excel files, consolidating data into a single cloud-based platform. Datarails automates critical financial processes including FX conversions, eliminations, hierarchies, financial adjustments, and more, while allowing end-users to continue working in Excel as they always have.
Serving primarily the US market, Datarails is experiencing strong and sustained growth. This is an exciting opportunity to help build a cutting-edge SaaS product at scale and shape how we build and deliver software in an AI-native way.
The Role
We’re looking for a senior engineer who is ready to move beyond traditional software development and take full ownership of product delivery.
This is not a classic backend engineering role. You will work directly with Product to turn ideas and requirements into production-ready solutions, make the technical decisions, and use AI coding agents as a core part of your engineering workflow.
You will own the outcome, not just the code. AI-generated code is your code, and you are accountable for its quality, security, performance, and maintainability.
What makes this role different
End-to-end ownership: Own features from requirements and technical design through implementation, testing, deployment, and production monitoring.
AI-native development: Use AI coding agents as a core part of your daily workflow across development, testing, debugging, documentation, and automation.
Engineering judgment: Review, challenge, and validate AI-generated solutions, making sure what gets shipped is correct, secure, scalable, and maintainable.
Product partnership: Product owns the what, you own the how. Work closely with Product to understand the problem, challenge assumptions, evaluate trade-offs, and find pragmatic technical solutions.
Broad technical ownership: Strong software engineering fundamentals and solid understanding of architecture and design principles. Python and Django will be the primary technologies, but you will take responsibility for whatever is required to deliver a feature, including frontend, QA, automation, infrastructure, and integrations
Outcome focused: Success is measured by the quality and impact of what you ship, not by the amount of code you write.
What you’ll do
Own product features end-to-end, from requirements through production and monitoring.
Work with Product to translate business problems into clear technical solutions and execution plans.
Design scalable and maintainable solutions, including architecture, APIs, data models, and integrations.
Use AI coding agents to accelerate implementation, testing, debugging, and automation.
Review and improve AI-generated code, ensuring high standards for correctness, security, performance, and maintainability.
Make independent technical decisions and communicate architectural trade-offs clearly.
Contribute across backend, frontend, QA, CI/CD, and infrastructure when needed to deliver a feature.
Investigate and solve complex production and performance issues.
Continuously improve how we build, test, and ship software using AI-native development practices.
About you
Must have
BSc in Computer Science or equivalent experience.
7+ years of experience building and shipping production software, with a strong track record of owning features or systems end-to-end.
Strong system design fundamentals and experience building scalable SaaS systems in the cloud.
Ability to make sound architectural decisions independently and understand technical trade-offs.
Solid understanding of REST APIs, asynchronous programming, distributed systems, microservices, and testing.
Hands-on experience with AI coding tools such as Claude Code, Cursor, Codex, GitHub Copilot, or similar.
Strong ability to review, challenge, and improve AI-generated code rather than relying on it blindly.
Strong product mindset, with the ability to balance technical and business priorities and focus on solving the underlying problem.
Strong ownership mentality and ability to drive work independently from idea to production.
Excellent communication skills and high proficiency in English, with the ability to work directly with Product and engineering teams.
Nice to have
Experience with modern frontend technologies, particularly Angular.
Experience with CI/CD pipelines and infrastructure-as-code tools such as Terraform or Helm.
Familiarity with Celery, RabbitMQ, event-driven architectures, or streaming systems.
Experience with cloud infrastructure, Docker, and Kubernetes.
Experience with QA automation and end-to-end testing.
