Data Workers, Inc.
Data Workers builds an autonomous agentic data platform that runs data operations across a company's existing stack. Its agents detect, diagnose, fix, review and verify data work while maintaining human approval, audit receipts and control over production changes.
At a Glance
- Enterprise data and analytics engineering teams
- Companies with roughly 100 to 1,500 employees, with larger organizations targeted by Enterprise
- Data platform leads and data engineering teams of approximately 2 to 15 people
- Financial services, healthcare and life sciences, retail/e-commerce, manufacturing, logistics, legal, HR and other data-intensive industries
- +2 more
AI Tools by Data Workers, Inc.
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Data Workers
Agentic Data Platform with MCP Agents
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Latest News
Published Data Agent Evals Report 2026 covering evaluation of data agents and industry benchmarks.
Published Pricing Report 2026 on the Agentic Data Industry, surveying 52 published pricing pages.
Released the Data Workers Community Edition: open-source autonomous data engineering agents under Apache 2.0.
Published an article explaining why data agents need a semantic/context layer to avoid hallucinating on enterprise data.
Products & Services
Free Apache-2.0 open-source core with specialized MCP agents for data engineering. It runs locally or in the customer's infrastructure, uses in-memory stubs by default, and is designed for read, analysis and recommendation workflows with optional governed writes.
A workforce of specialized data agents that read, write and act across cloud, warehouse and data-stack tools. It covers incidents, pipelines, quality, schema, governance, migration, streaming, observability, ML, catalog and usage intelligence.
A provenance-stamped semantic and knowledge graph that provides a governed context layer across Snowflake, Databricks, BigQuery, dbt, catalogs, glossaries and semantic layers; the product page describes 50+ connectors.
An orchestrator that runs the detect-diagnose-fix-review-verify loop, sequences specialist agents, reasons across quality, schema, lineage and cost, and supports a configurable autonomy dial, blast-radius scoping and reversal.
Market Position
Data Workers positions itself as a vendor-neutral, cross-cloud platform that closes the full detect-to-verified-fix loop, rather than a single-step observability, catalog, BI or coding-assistant tool. Its own comparison material names Monte Carlo, Datafold and Validio, Recce, Databricks Genie, Snowflake Cortex, Upriver, Atlan, Ataccama and OpenMetadata among adjacent or competing products; Tracxn also identifies Fivetran, Matillion and Nexla as competitors. Its differentiators are agentic remediation, a living context graph, governed writes with receipts, MCP compatibility and published pricing.
Leadership
Founders
Dhanush Shetty
Founder of Data Workers; the company's evaluation guide identifies him as founder. The available source material does not document prior roles or companies.
Executive Team
Dhanush Shetty
Founder
Founder and author of Data Workers' evaluation and market-research materials; the sources reviewed do not specify additional executive titles or prior employers.
Founding Story
The company says it spent four months before writing product code studying earnings calls, vendor acquisitions, market gaps and interviewing data engineers. It concluded that data teams were still firefighting repetitive work such as alert triage, lineage tracing and schema validation, and chose to build vendor-neutral specialized agents that sit on top of existing warehouses and tools rather than replace them.
Business Model
Revenue Model
Paid enterprise software and services, combining a one-time six-week pilot with annual Scale and Enterprise subscriptions. Pricing is not metered by agents, runs, tasks or seats; customers bring their own model key and pay model providers directly. The open-source core is free.
Pricing Tiers
Apache-2.0 open-source core; self-hosted, no usage cap or expiry, and no card required.
Six-week pilot; unlimited seats, runs in the customer's infrastructure, with a named engineer. The evaluation guide says the fee is credited in full against year one.
Annual term, unlimited seats, customer's infrastructure, 49 connectors and included agents/governance.
Annual term, unlimited seats, deployment in the customer's VPC, cloud or on-premises, with enterprise deployment and security options.
Target Markets
- Enterprise data and analytics engineering teams
- Companies with roughly 100 to 1,500 employees, with larger organizations targeted by Enterprise
- Data platform leads and data engineering teams of approximately 2 to 15 people
- Financial services, healthcare and life sciences, retail/e-commerce, manufacturing, logistics, legal, HR and other data-intensive industries
- Teams operating Snowflake, BigQuery or Databricks with dbt and multiple data tools
- Organizations needing governed automation, private networking, dedicated VPC or on-premises deployment
- Data incident root-cause analysis and remediation
- Pipeline construction, validation and self-healing
- Schema-change detection, impact analysis and migration
- Data quality monitoring and remediation
- Cataloging, discovery, semantic context and lineage
- Data access, governance, security and compliance evidence