Full-Time
Datafold
11-50 employees
Unified data quality platform for testing
Compensation Overview
$175k - $245k/yr
Senior
Remote in USA
Candidates must be based in the USA.
- 5+ years as a software engineer with a strong backend focus
- Proficiency in Python is required
- Experience building with LLMs is a plus
- Proven ability to manage projects end-to-end, from design through deployment
- Thoughtful about balancing speed, quality, and business impact
- Drive the development of DMA, shaping both the technical and architectural foundation for a product that combines AI and data engineering to solve complex migration challenges
- Design, develop, and maintain backend systems focused on AI-driven SQL translation and cross-database data comparison
- Collaborate with early customers to refine features and iterate toward product-market fit
- Make strategic technical decisions to ensure DMA is scalable, robust, and optimized for high-performance data migration
- Take ownership of projects end-to-end, troubleshooting and resolving complex challenges in real time to deliver high-quality, impactful results
- Experience building with LLMs is a plus
Datafold provides a platform that focuses on maintaining high data quality through proactive and automated testing. Its tools help data teams identify and resolve potential data issues before they can affect the data warehouse, which is different from traditional observability tools that mainly detect problems after they occur. Datafold integrates into the development cycle, allowing for automated testing during deployments, migrations, and ongoing monitoring. This approach helps teams enhance their development speed while ensuring data integrity and reliability. The company operates on a subscription-based model, generating revenue through recurring payments from its clients.
Company Size
11-50
Company Stage
Series A
Total Funding
$22.3M
Headquarters
San Francisco, California
Founded
2020
Simplify's Take
What believers are saying
- Growing demand for automated data quality solutions as data complexity increases.
- Partnerships with companies like dbt Labs enhance data testing capabilities.
- Investment interest highlighted by Datafold's $20M Series A funding.
What critics are saying
- Increased competition from companies like Monte Carlo focusing on data observability.
- Complex data pipelines lead to more frequent data quality incidents.
- Open-source tools may cannibalize subscription-based revenue model.
What makes Datafold unique
- Datafold integrates deeply into the development cycle, preventing bad code deployments.
- It offers automated testing during deployment, migrations, and ongoing monitoring stages.
- Datafold's platform supports instant table comparison and profiling for data QA.
Help us improve and share your feedback! Did you find this helpful?
Benefits
Remote Work Options
Growth & Insights and Company News
6 month growth
↑ 0%1 year growth
↑ 0%2 year growth
↓ -8%Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More. Data is critical to every business, but when the volume of the information and the complexity of pipelines grow, things are bound to break!According to a new survey of 200 data professionals working in the U.S., instances of data downtime — periods when enterprise data remains missing, inaccurate, or inaccessible — have nearly doubled year over year, given the surge in the number of quality incidents and the firefighting time taken by teams.The poll, commissioned by data observability company Monte Carlo and conducted by Wakefield Research in March 2023, highlights a critical gap that needs to be addressed as organizations race to pull in as many data assets as they can to build downstream AI and analytics applications for business-critical functions and decision-making.“More data plus more complexity equals more opportunities for data to break. A higher proportion of data incidents are also being caught as data is becoming more integral to the revenue-generating operations of organizations. This means business users and data consumers are more likely to catch incidents that data teams miss,” Lior Gavish, co-founder and CTO of Monte Carlo, tells VentureBeat.
Last week Datafold launched an open source package for diffing data between databases e.g. PostgreSQL <> Snowflake.
To further strengthen our commitment to providing industry-leading coverage of data technology, VentureBeat is excited to welcome Andrew Brust and Tony Baer as regular contributors. Watch for their articles in the Data Pipeline. New York-headquartered data reliability company Datafold has launched an open-source diffing tool to help enterprises compare databases and perform checks to validate data consistency
After serving leading enterprise data teams at Patreon, Thumbtack, and others for the past few years, Datafold is now launching a free-tier and transparent cloud pricing.
Simplifies Achieving Full Test Coverage Across All Schemas and Pipelines Data quality is swiftly dealt with in pull requests Regression testing on all tables across pipelines without writing additional SQL Instant setup with dbt Cloud or with a Python SDK for dbt Core Datafold, a data quality platform that automates the most tedious parts of [] The post Datafold Partners With dbt Labs, Launches Integration to Enable Analytics Engineers to Deliver Trusted Data Faster appeared first on MarTech Series.