We design and implement data engineering solutions for companies in Spain and worldwide: modern data architectures, ETL/ELT pipelines, cloud migration, heterogeneous source integration and data infrastructure preparation to scale with artificial intelligence. We transform dispersed data into strategic assets that drive faster, smarter business decisions.
Data Engineering Services for Companies in Spain
At MiT Software we develop custom data engineering solutions for mid-size and large companies that need to centralize, structure and activate their data to make better decisions and build artificial intelligence capabilities on top of it. Many companies accumulate years of valuable data dispersed across legacy systems, spreadsheets, isolated databases and departmental applications that were never connected. Our mission is to transform that chaos into a reliable, scalable data infrastructure ready for analytics and AI. We work with the most advanced technologies on the market — Snowflake, Databricks, dbt, Apache Spark, Apache Airflow, AWS Glue, Azure Data Factory — and design architectures tailored to the volume, complexity and specific objectives of each company.
We start with a comprehensive analysis of your current data ecosystem: what sources exist, what systems store them, what quality they have, how they flow between systems and what analytics and AI needs the organization has. This diagnosis defines the target architecture and the implementation roadmap prioritized by impact and feasibility.
We define the most appropriate data architecture for your organization's objectives and context: which technologies to use, how to structure the data layers, which governance model to apply and how to ensure the architecture is scalable, maintainable and aligned with industry best practices.
We build the pipelines that move data from its original sources to the analytics platform, applying the necessary transformations to clean, enrich and structure the data according to the defined model. All pipelines are developed with DataOps practices: versioning, testing, documentation and monitoring from day one.
We execute the migration of historical data from your current systems to the new platform with exhaustive integrity validation, record reconciliation and quality testing before completing each migration phase. The process is progressive and controlled to ensure operational continuity.
We connect the new data platform to the analytics and visualization tools that generate the most value for your organization: Power BI, Tableau, Looker, Metabase or any other BI tool, configuring the semantic models and dashboards that allow business teams to exploit data autonomously.
We train technical and business teams in the use and maintenance of the new data platform. We deliver complete documentation of the architecture, pipelines and data models so your organization can operate and evolve the infrastructure autonomously without depending on us for day-to-day operations.
Most companies have their data dispersed across dozens of systems that never communicate with each other. We build modern data architectures that centralize all organizational information — sales, operations, customers, finances — into a single accessible, reliable and real-time updated platform, eliminating the data silos that slow down decision-making.
Artificial intelligence only works well when the data feeding it is clean, structured and accessible. We build the data infrastructure you need to train machine learning models, implement RAG systems and deploy AI solutions with confidence, ensuring your AI projects are built on a solid and sustainable data foundation.
We design and implement the most appropriate data architecture for your organization: a modern Data Warehouse in Snowflake or BigQuery for structured analytics, a Data Lake in S3 or Azure Data Lake for large-volume unstructured data, or a Lakehouse architecture that combines the best of both worlds with platforms like Databricks or Delta Lake.
We build robust, monitored and versioned data pipelines that move, transform and load data from any source into your analytics platform. We use dbt for declarative SQL transformations, Apache Airflow for flow orchestration and Apache Spark for processing large volumes of distributed data.
We connect and unify data from any source: relational and NoSQL databases, third-party APIs, ERP and CRM systems, flat files, real-time streams, IoT sensors or any other data source that exists in your organization, regardless of its format, protocol or update frequency.
We migrate your data from legacy systems — on-premise servers, old databases, obsolete data warehouses — to modern cloud platforms like Snowflake, Databricks, AWS Redshift or Google BigQuery, with detailed migration plans, data integrity validation and zero data loss during the process.
We implement data quality frameworks that automatically detect anomalies, inconsistencies and missing values before they reach analytics systems or AI models. We establish data governance policies, data dictionaries and lineage systems that guarantee complete traceability of each data point from its origin to its final use.
For companies that need to act on data the moment it is generated, we build streaming architectures with Apache Kafka, AWS Kinesis or Azure Event Hubs, combined with real-time processing engines like Apache Flink or Spark Streaming, to detect anomalies, trigger alerts and make decisions in milliseconds.
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