Your data stack, engineered to deliver.
We design, migrate, and staff modern data platforms — Snowflake, Databricks, and the AI layer on top. Benchmarked before cutover, documented on handover, owned by your team afterward.
01
Sources
- Postgres · Salesforce
- S3 · APIs
02
Ingest
- Fivetran · Airbyte
- Airflow
03
Transform
- dbt · Spark
- Snowflake · Databricks
04
Activate
- BI · Reverse ETL
- ML & LLM layer
We work in
- Snowflake
- Databricks
- dbt
- Fivetran
- Airflow
- AWS
- Google Cloud
- Azure
Three ways we engage
Most engagements start with one and grow into another. We scope them separately so you can buy only what you need.
Modern Data Stack implementation
From a legacy warehouse and a pile of scheduled scripts to a production-grade lakehouse: ingestion you can trust, dbt models under test, orchestration with real alerting, and a semantic layer the business actually reads.
- ELT with Fivetran or Airbyte
- dbt project structure, tests, and CI
- Airflow or Dagster orchestration
- Semantic layer and BI handoff
OutcomeNightly runs that finish before the business day, with failures that page someone instead of going unnoticed.
Snowflake ↔ Databricks migrations
Our flagship engagement, in both directions. We benchmark your real workloads on the target platform and model the cost before anyone commits to a cutover date, then run both systems in parallel until the numbers agree.
See the four-phase process- Workload inventory and dependency mapping
- SQL dialect translation with a test harness
- Parallel run with row-level reconciliation
- Cost model built from your actual query history
- Cutover with a documented rollback path
OutcomeZero-downtime cutover, with a cost model you saw before you signed off.
Data & AI staff augmentation
Senior LATAM engineers working US business hours, vetted by people who do the work rather than by a keyword filter. They join your standups, your repo, and your on-call rotation.
See profiles and pricing model- Data, Analytics, ML, and Platform Engineers
- Overlapping US time zones, not offshore handoffs
- Technical screening by practitioners
- Start in two to three weeks
OutcomeA senior engineer contributing to your repo within the first sprint.
What the work produces
PLACEHOLDER — reemplazar por casos reales. Los números de abajo son de ejemplo y NO deben publicarse como resultados propios hasta que correspondan a un proyecto real y puedas respaldarlos.
38%
warehouse cost reduction
PLACEHOLDER: Snowflake to Databricks, mid-market retailer
PLACEHOLDER — describí acá el caso real: punto de partida, qué se migró, en cuánto tiempo y cómo se midió el resultado. Sin cliente identificado si no tenés permiso escrito para nombrarlo.
Databricks · dbt · Airflow
4h → 22m
nightly pipeline runtime
PLACEHOLDER: Legacy warehouse to lakehouse
PLACEHOLDER — describí acá el segundo caso real: qué se rediseñó, qué se eliminó, y qué métrica mejoró de forma verificable.
Snowflake · dbt · Fivetran
3 weeks
to first engineer contributing
PLACEHOLDER: Embedded data engineering team
PLACEHOLDER — describí acá un caso de staffing: qué perfil, cuánto tardó el onboarding, y qué entregó en el primer trimestre.
Databricks · Spark · Python
Why Integral Data
Four reasons that tend to matter to the people who sign off on this work.
- Practitioners, not a bench
- The person scoping your migration is the person who will run it. Technical screening for staffing is done by engineers who have shipped the same work.
- US business hours
- Our engineers work in overlapping US time zones. You get same-day answers in your standup, not an overnight handoff queue.
- Benchmarked, not guessed
- We do not quote a migration from a vendor's marketing page. We run your workloads on the target platform and build the cost model from your own query history.
- Yours on handover
- Infrastructure as code, dbt projects under test, and runbooks in your repository. The measure of a good engagement is that you do not need us afterward.
Who is behind this
REEMPLAZAR ESTE TEXTO desde el panel de administración.
Escribí acá dos o tres frases sobre la trayectoria del fundador en datos e IA: años de experiencia, tipos de plataforma construidos, e industrias. Este bloque es el que más credibilidad aporta en una venta B2B técnica, y hoy es un marcador de posición.
Book a technical call
Thirty minutes with an engineer, not a sales call. Bring your current stack and the problem you are trying to solve, and you will leave with a straight answer on whether we are a fit and what it would take.
- A working session, not a pitch deck
- A rough shape of the approach and effort
- An honest no if this is not our work
- No follow-up sequence if you go quiet