Data engineering and BI: a single source of truth to decide with data
We connect your systems (ERP, CRM, e-commerce, spreadsheets) into a data warehouse, model the data and build dashboards everyone actually looks at. No more "the ERP says one thing and the CRM another". In your cloud (Azure/AWS), with the infrastructure and code 100% yours.
Data engineering is the discipline of moving, cleaning and organizing your data so it serves decisions.
We ingest information from your sources with ETL/ELT pipelines, consolidate it into a data lake or data warehouse, model it so it makes business sense, and expose it in BI dashboards.
The code and configuration are 100% yours from day one.
Six operational reasons, zero adjectives
The code is yours from day one
Repos in your name, documented CI/CD and zero vendor lock-in. If you leave tomorrow, you take it all, running.
WhatsApp API with an official provider
We are a Meta Tech Provider: your WhatsApp Business API line with no middlemen, and chatbots wired to your ERP.
Sprint delivery, CMMI 2 processes
A working demo every two weeks and measurable progress. No "it’s 80% done" without something you can click.
AI applied to your operation
LLM agents, RAG over your data and process automation — the same practice we use to run iTech itself.
Real nearshore: Texas + Monterrey
Legal entity in the U.S. (iTech Corp, Texas), contracts under U.S. law, same CST time zone and USMCA.
ERP with CFDI 4.0 invoicing
We implement Odoo with integrated SAT stamping (PAC), client portal and reconciliation — a full operation, not just software.
When you need it
What's included
Ingestion and ETL/ELT
Connectors to your sources (ERP, CRM, SQL/NoSQL databases, APIs, files and spreadsheets) that extract data in batches or streaming, without touching your production systems.
Data lake / warehouse
A central repository — data lake for raw data, warehouse for analysis-ready data — in your cloud (Azure, AWS) or on PostgreSQL/Snowflake, sized to your volume.
Data modeling
Dimensional design (facts and dimensions) with dbt: clear definitions of "what a customer is", "what a sale is" and "what counts as an asset", versioned and documented.
Orchestrated pipelines
Automated flows with Airflow that run on schedule, retry on failure and alert when something breaks. No manual processes that depend on one person.
BI dashboards
Dashboards in Power BI or Metabase with the indicators that matter to each area, filters by period and region, and role-based access. Built to decide, not just to look at.
Data quality and governance
Automated consistency tests, gap and duplicate detection, a data catalog, domain ownership and access control. A source of truth is only useful if it is trustworthy.
Data observability and lineage
Pipeline monitoring (freshness, volume, schema), alerts when a load fails or arrives late, and lineage showing which source and transformation each number came through. So when someone asks "where does this figure come from?", there is a traceable answer.
Data activation (reverse ETL)
When you need it, we push modeled data back into operational tools — CRM, marketing platform, ERP — so a segment or a score computed in the warehouse reaches where action happens, not just a dashboard someone looks at.
How we work
1Source and business-question discoveryDeliverablea source inventory, agreed indicators and a fixed-budget scope
We map where your data comes from and, above all, what decisions you want to make with it. Without those questions there is no useful dashboard.
2Architecture and data modelDeliverablean architecture diagram, the dimensional model and a metrics dictionary validated by the business
We define the architecture (lake, warehouse, orchestration) in your cloud and the dimensional model. We agree on single definitions for each metric before moving a single row of data.
3Pipelines and ingestionDeliverableorchestrated pipelines in production with their history loaded and quality tests active
We build the connectors and the ETL/ELT pipelines with dbt and Airflow. We load an initial history and leave the updates running automatically.
4Dashboards and validationDeliverablepublished per-area dashboards, reconciled with your figures, and a training session to read and filter them
We stand up the dashboards in Power BI or Metabase and validate them against your current numbers until they match.
5Quality, governance and observabilityDeliverablea data catalog with domain owners, quality tests, and freshness alerts operational
We turn on the quality tests, the catalog, access control and pipeline monitoring with alerts.
6Handover and evolutionDeliverablethe full repository in your cloud, an operations runbook and 90 days of support — 100% yours, no vendor lock-in
We hand over the code, infrastructure as code and documentation, and leave a prioritized backlog of new metrics.
Tech stack
The tools and platforms we build it with — chosen for your problem, not for hype.
Frequently asked questions
Can't find your question? Talk to an engineer — no sales script.
Contact us →Can you build it in my own cloud (Azure or AWS)?
Yes. We work on Azure and AWS, the clouds where we have the most experience, and the data stays in your own subscription. All infrastructure is defined as code (Terraform) and is 100% yours: if you switch providers tomorrow, you take everything without depending on us.
Does it connect with my current systems (ERP, CRM, e-commerce)?
Yes. Ingestion runs through connectors and APIs into your sources — ERP, CRM, e-commerce, SQL/NoSQL databases, files and spreadsheets — in read-only mode, without modifying or putting your production systems at risk. If a source has no API, we read from its database or from scheduled exports.
How long does it take to be ready?
It depends on the number of sources and how clean the data is. A first warehouse with a couple of sources and a set of dashboards usually takes 6 to 12 weeks; integrations with many systems or very messy data take longer. We define it with fixed scope and budget in the assessment, and deliver in phases so you see value early.
Do I need real-time processing, or is batch enough?
Most business reporting is solved very well with batch loads every hour or every day — simpler, cheaper and easier to maintain. Real-time (streaming) makes sense when a decision cannot wait minutes, like operational monitoring or fraud detection. We recommend what your case needs, not what sounds more impressive.
What about data quality if my sources come in "dirty"?
That's normal and we plan for it. The pipelines include cleaning, deduplication and automated consistency tests, and the catalog makes clear what each data point means and who owns it. When bad data comes from the source, we flag it: the system improves trust in the numbers, but the root fix sometimes lives in the process that captures that data.
Do I own the code and infrastructure?
Yes, 100%. The dbt models, Airflow DAGs, dashboards and infrastructure as code (Terraform) live in your repository and your cloud from the first commit. We work with a CMMI Level 2 certified process: if tomorrow you want to run it with your own team or another provider, you have everything you need, with no vendor lock-in.
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