Data infrastructure
that scales.
ETL pipelines, data warehouses, and transformation layers — the foundation that makes analytics, AI, and automation possible.
The full data engineering stack.
From raw data ingestion to clean, tested, documented models ready for your BI tools and AI workloads — we build the entire data layer.
ETL/ELT Pipelines
Extract, transform, and load data from any source — SaaS APIs, databases, file systems, and streaming events. Batch and real-time pipelines orchestrated with Airflow, Prefect, or Fivetran. We build for reliability: idempotent runs, backfill support, and alerting when something breaks.
Data Warehousing
Snowflake, BigQuery, Redshift, or PostgreSQL — we design schemas, define table structures, optimize query performance, and manage costs. We choose the right warehouse for your volume, query patterns, and budget. Schema design today determines analytics performance for the next three years.
dbt Transformations
Modular SQL transformations with testing, documentation, and version control. Analytics engineering done right — your analysts write SQL, we make it production-grade.
Real-Time Streaming
Kafka, Kinesis, or Pub/Sub for event-driven data pipelines. Real-time dashboards, anomaly alerts, and operational integrations that act on data as it arrives.
Data Quality & Observability
Automated testing, freshness monitoring, row count checks, and anomaly detection. Know when your data breaks before your dashboards show wrong numbers.
Reverse ETL
Push warehouse data back to operational tools — CRM, ad platforms, email, Slack. Close the loop between analytics insight and business action.
Data Catalog & Governance
Column-level lineage, ownership tagging, access controls, and PII detection. Know what data you have, where it came from, and who can touch it.
Analytics Engineering
We bridge data engineering and analytics — building the transformation layer that turns raw warehouse data into clean, documented models your BI tools can actually use.
Built for teams that need data they can trust.
Data engineering pays off when bad data is costing you decisions, analyst time, or model accuracy. Here's who we help most.
Data teams that need clean, reliable pipelines
Your analysts spend 60% of their time cleaning data instead of analyzing it. You need infrastructure that delivers clean, tested, documented data — so your team can focus on insights.
Companies outgrowing spreadsheets
Your Excel workbooks are 50MB and crashing. Your team maintains 15 versions of 'the source of truth.' You need a real data platform — and a team to build it fast.
AI and ML teams that need clean training data
Model quality is data quality. If your AI is making bad predictions, the problem is usually upstream — incomplete, inconsistent, or stale data. We fix the foundation.
Analytics engineers adopting dbt
You've heard about dbt and want to modernize your transformation layer. We help teams adopt dbt properly — project structure, testing strategy, documentation, and CI/CD for SQL.
From data audit to production pipeline.
Assess
We audit your current data sources, existing pipelines, and analytics requirements. We identify data quality issues, gaps in coverage, and quick wins. The output is a prioritized roadmap with ROI estimates.
Architecture
We design your data platform: warehouse selection, schema design, pipeline orchestration approach, transformation strategy, and observability plan. We present the architecture for review before building anything.
Build
We build pipelines, write dbt models, configure orchestration, and set up data quality tests. Everything is version-controlled, documented, and deployed through a proper CI/CD pipeline.
Operate
We hand off with full documentation, runbooks, and alerting configured. Optionally, we provide ongoing support — monitoring, incident response, and new source integration as your data needs grow.
Data engineering without the big-company overhead
We've built data platforms for companies from Series A to enterprise — Snowflake, BigQuery, dbt, Airflow, Kafka. We know how to right-size the architecture for your stage. US entity for contracting, LATAM engineering for execution — senior data engineers at 40–60% less than US agency rates.
Architecture-first approach
Design reviewed and approved before we build — no surprise rewrites
LATAM data engineers, US pricing advantage
Senior engineers from Colombia, Argentina, Mexico
Questions we hear about data engineering.
Do we need a data warehouse if we're small?
If you have 5+ data sources and need cross-system reporting, yes. Modern warehouses (BigQuery, Snowflake) are pay-per-query — you can start small for under $100/month. The bigger cost is the time your team spends manually pulling and merging spreadsheets. A warehouse eliminates that and gives your analysts a single source of truth.
What's the difference between ETL and ELT?
ETL transforms data before loading it into the destination (traditional approach — common with on-prem systems). ELT loads raw data first, then transforms it inside the warehouse using SQL (modern approach — usually cheaper, more flexible, and easier to debug). We typically recommend ELT with dbt because it's faster to iterate, easier to test, and the raw data is always available for reprocessing.
How long does it take to build a data pipeline?
A basic pipeline (3–5 sources to warehouse to dashboard) takes 3–5 weeks. Enterprise data platforms with real-time streaming, data quality monitoring, and reverse ETL take 8–16 weeks. We always start with a data audit and architecture design phase before building, so you know exactly what you're getting before we write a line of code.
Ready to build data infrastructure that works?
Tell us where your data currently lives and what decisions it needs to support. We'll audit your current stack, design the right architecture, and build pipelines that your analysts and AI systems can actually rely on.