Matia Raises $21M to Help Enterprises Consolidate Their Data Management Operations
•February 10, 2026
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Companies Mentioned
Why It Matters
Consolidating fragmented data tools lowers costs and accelerates AI‑ready data pipelines, a critical advantage for scaling enterprises.
Key Takeaways
- •$21M Series A led by Red Dot Capital.
- •Platform unifies ETL, reverse ETL, observability, catalog.
- •Supports 100+ sources, syncs to Snowflake, Databricks, BigQuery.
- •Cuts total cost of ownership ~78% for customers.
- •AI data engineer auto‑creates pipelines, detects anomalies.
Pulse Analysis
The enterprise data landscape has become increasingly fragmented, with teams juggling separate tools for ingestion, monitoring, cataloging and activation. This tool sprawl drives operational overhead and hampers the speed at which trusted data can be fed to AI models. Matia’s unified DataOps platform, built on Amazon Web Services, tackles this challenge by consolidating core functions into a single pane‑of‑glass interface. The recent $21 million Series A, led by Red Dot Capital, underscores investor confidence that a consolidated stack will become the backbone of next‑generation analytics and AI workloads.
At the heart of Matia’s offering is a real‑time replication engine that connects to more than 100 data sources—including databases, SaaS applications and APIs—and streams changes into major warehouses such as Snowflake, Databricks and BigQuery. The platform’s reverse‑ETL capability pushes curated insights back into operational tools, while its built‑in observability suite continuously monitors data quality and alerts on pipeline failures. Customers report up to an eight‑fold reduction in synchronization time and an average 78 percent drop in total cost of ownership, illustrating the tangible efficiency gains of a unified stack.
The next phase of Matia’s roadmap centers on an AI‑powered data engineer that can automatically design pipelines, surface anomalies and perform impact analysis without human intervention. By embedding intelligence directly into the data layer, the solution promises to shorten the time‑to‑value for AI projects and reduce reliance on specialized engineering talent. As enterprises accelerate AI adoption, platforms that deliver both operational simplicity and built‑in analytics intelligence are likely to become strategic assets, positioning Matia to compete with legacy data‑integration vendors and emerging cloud‑native rivals.
Matia raises $21M to help enterprises consolidate their data management operations
Unified data operations startup Matia Inc. wants to give enterprises a better way to manage their data pipelines at scale after raising $21 million in an early‑stage A round of funding.
Red Dot Capital led the Series A round, which saw participation from existing backers such as Leaders Fund, Secret Chord Ventures, Cerca Partners, Caffeinated Capital and VelocityX, plus angel investors including Karim Atiyeh of Ramp Network Inc. and Alex Pham of Toyota Motor Corp. To date, Matia has now raised more than $31 million in funding.
Matia is the creator of a unified DataOps platform built on Amazon Web Services that’s designed to consolidate modern data infrastructure stacks into a single interface. It does this by combining data extract/transact/load processes with reverse ETL, data observability and a data catalog, helping teams to reduce tool bloat and speed up their data workflows.
The platform is able to replicate data in real time from more than 100 sources, including popular databases, software‑as‑a‑service platforms and application programming interfaces, to data‑warehouse platforms such as Snowflake, Databricks and BigQuery. Its reverse ETL capability means it can activate this information by pushing insights from the data warehouse back into operational SaaS tools used by business teams. With its data observability suite, it offers proactive monitoring of data quality, with alerts for any issues such as data pipeline failures.
Matia says teams will benefit from having all of their data operations visible through a single pane of glass, replacing the need to manage multiple tools for ingestion, monitoring and cataloging. Because it can detect data anomalies and errors immediately upon ingestion, it helps to prevent bad or inaccurate data from reaching downstream applications, while its support for parallel synchronization helps it to reduce data pipeline syncing times by up to eight times.
With today’s funding, Matia said it intends to build upon this foundation with the development of a new artificial‑intelligence‑powered data engineer. It’s essentially an AI agent that’s designed to automatically create data pipelines, detect anomalies and perform impact analysis, among other data‑management tasks.
Matia co‑founder and Chief Executive Benjamin Segal said it’s time for data engineering to enter the AI era, because AI itself requires vast amounts of trusted data and system‑wide context. “Matia delivers an AI‑ready data layer in one unified platform, replacing fragmented point solutions that lack context,” he explained.
By consolidating all of their data tools into Matia’s unified platform, instead of maintaining separate ingestion, observability and activation systems, customers have reduced their total cost of ownership by about 78 % on average, the company said.
Danielle Ardon Baratz of Red Dot Capital said Matia is redefining the data stack for AI workloads. “It stands out by consolidating critical data functions into a single platform that actually reduces operational overhead,” she said.
Segal said Matia has gained strong momentum over the last year, growing its revenue by more than 10 times after winning a deluge of new customers. They include the digital payments firm Ramp, compliance‑automation startup Drata Inc., freelancer‑focused business‑management platform HoneyBook Inc. and Lemonade Insurance Co.
“We’re seeing a clear shift in how teams think about their data infrastructure,” Segal said of that growth. “As companies scale, they want fewer tools, more shared context and systems that hold up under production demands.”
Image: SiliconANGLE/Nano Banana
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