Eigen X + Matillion

ETL to power your data warehouse

Most organizations pulling data from dozens of source systems eventually run into the same bottleneck: building and maintaining ETL pipelines by hand demands specialized engineering time. Eigen X closes that gap by implementing Matillion, a cloud-native ETL/ELT platform that lets your team design data pipelines visually, with drag-and-drop job building instead of custom code. Because Matillion uses push-down processing, transformations run directly inside your cloud data warehouse, so millions of rows move and transform in seconds using compute power you already have. With 80+ pre-built connectors, the ability to build custom ones for niche systems, and built-in collaboration and version control, Matillion gives your organization a faster, more governed path from raw source data to analytics-ready insight.

  • Certified Matillion Associate

  • Matillion ETL Foundations Certified

  • Marketing Cloud Administrator

    Certified Matillion Practitioner

Open Data Infrastructure Explained

A Valued Partnership

As a partner of Matillion, we have implemented the ETL tool within several projects across industries. By leveraging the power of our relationship with Matillion, our certified developers can guide you through best practices and can address and expedite issues with access to Matillion resources and solutions engineers.

Our Key Matillion Expertise

  • With more than 80 pre-built connectors spanning the SaaS applications, databases, and files that businesses run on, plus the ability to build custom connectors, Matillion streamlines your data transformation. Instead of engineering a one-off integration for every new source, Eigen X implements Matillion to pull data into a usable pipeline to get actionable insights the same day.

  • Matillion uses a push-down ELT approach that hands the heavy lifting to your cloud data warehouse's own compute engine, processing millions of rows in seconds rather than staging data through a separate processing layer. On top of that engine sits a drag-and-drop, browser-based interface with live feedback, validation, and data previews built directly into the workflow. That combination means our team can design, test, and adjust sophisticated data pipelines visually, in minutes.

  • Matillion isn't tied to a single cloud ecosystem. It integrates natively with Snowflake, Databricks, Amazon Redshift, Google BigQuery, and Azure Synapse Analytics, so it fits the platform a business has already invested in rather than forcing a migration to use it. That flexibility means one consistent orchestration layer, regardless of which cloud warehouse sits underneath, and room to add or shift platforms later without rebuilding pipelines from scratch.

  • dbt State reduces the compute a dbt job needs by an average of 30%, by checking whether source data actually changed before rebuilding a model rather than reprocessing everything on every run. dbt Wizard, a purpose-built AI agent that understands a project's existing contracts and lineage, can propose and validate transformation logic changes without putting downstream reliability at risk.

  • Fivetran and dbt combine into what the companies call Open Data Infrastructure: a single, standards-based path from raw source data to trusted, AI-ready models, with no proprietary lock-in at any step. A shared Agents Schema carries metric definitions, semantic models, lineage, and business documentation across both platforms, so the context your team builds while defining a metric or modeling a business process isn't stranded in one tool. It becomes a common foundation that both human analysts and AI agents and assistants can rely on with confidence.