Foundation
Data architecture & warehousing
Cloud data platforms done right on AWS (Snowflake, Redshift, or BigQuery) with modeling, governance, and a semantic layer so every number means the same thing.
CapabilitiesData
Most data never makes it into the product. We turn it into one.
We design the pipelines, warehouses, and AI-ready foundations that turn raw, scattered data into products that decide, personalize, and scale, built on AWS by senior engineers.
Data, productized.
Most companies are data-rich and decision-poor.
The data exists. It’s just scattered, inconsistent, and stuck in tools that don’t talk to each other. Rootstrap builds the foundation (ingestion, warehousing, quality, modeling) and the layer on top that turns it into analytics, personalization, and production AI. Not a dashboard nobody opens. Data that ships inside the product.
What we actually deliver
Pipelines
ETL/ELT & big-data processing
Reliable batch and streaming pipelines (Spark, Kafka, Airflow, dbt, and AWS Glue/Kinesis) built to scale and instrumented so you know the moment something breaks.
Activate
Analytics, enrichment & ML features
Dashboards people actually use, data quality and enrichment, reverse-ETL back into the tools teams work in, and feature pipelines that power personalization and AI.
When your data needs to…
When your data needs toUnify data scattered across tools and databases
We buildELT pipelines into a governed warehouse (Snowflake, Redshift, or BigQuery)
Business outcomeOne source of truth your whole team can trust
When your data needs toAct on data in real time, not yesterday’s batch
We buildStreaming pipelines with Kafka, Kinesis, and Spark
Business outcomeProduct features and decisions that react as events happen
When your data needs toClean and enrich messy, inconsistent data
We buildData-quality, deduplication, and enrichment workflows
Business outcomeAnalytics and AI that run on data you can rely on
When your data needs toPersonalize the product for every user
We buildFeature pipelines and recommendation systems
Business outcomeExperiences that adapt and compound in value
When your data needs toLet teams answer their own questions
We buildModeling with dbt, a semantic layer, and self-serve dashboards
Business outcomeInsight without waiting on the data team
We build the pipeline, not just the dashboard.
A dashboard is the easy part. The hard part is the foundation underneath it: clean, governed, real-time data your team and your models can trust. That’s what we engineer first, so everything you build on top of it actually holds.
The Data Discovery Sprint
Every data engagement starts with discovery, a structured engagement where we map your data and design the warehouse, pipelines, quality, and governance before anyone builds a thing.
Data readiness map
What data you have, where it lives, what’s missing, and what must be cleaned before it can drive decisions or models.
Architecture recommendation
Warehouse, pipeline, and orchestration choices (Snowflake vs Redshift vs BigQuery, batch vs streaming) with the engineering rationale, on AWS.
Pipeline & quality plan
The ETL/ELT design, data-quality tests, and the modeling and semantic layer, so analytics and AI run on trustworthy data.
Governance & compliance
Access controls, lineage, and GDPR/HIPAA-aware handling, designed before the build, not bolted on after.
Get the foundation right and everything you build on top of it gets easier.
Governance, quality & compliance
Governed
Modeling, access controls, and a semantic layer so the numbers mean the same thing everywhere.
Observed
Lineage, freshness checks, and alerting, so you know when a pipeline breaks before the dashboard lies.
Compliant
GDPR- and HIPAA-aware data handling, in SOC 2 certified environments.
Trusted
Automated data-quality tests and reconciliation so analytics and AI run on data you can rely on.
The data stack we build on
AWS-first, with the modern data tools your team already knows.
AWS
Snowflake
BigQuery
Redshift
AWS Glue
Kinesis
Amazon S3- Amazon EMR
Apache Spark
Kafka
Airflow- dbt
PostgreSQL- Segment
Looker- Tableau
Where we’ve shipped data in production
EdTech
We migrated Emeritus off a legacy Salesforce-based system onto higher-performing data and web platforms, with SSO and a scalable CI/CD pipeline as the team grew to 40+.
Health-tech
For EXI we built wearable-data flows (Apple Watch, FitBit) feeding clinician dashboards through a secure, penetration-tested, GDPR/HIPAA-compliant data portal.
eCommerce & retail
For some of our main e-commerce clients we engineered data flows (Segment, SplitIO) and order/shipment platforms on AWS that held up through record Black Friday traffic.
Logistics & operations
For Kinimatic we turned warehouse data into natural-language insights and AI-driven recommendations across multiple sites.
Proof, in production
View case →
Platform migration & data systems
View case →
Wearable data + clinician dashboards
Let’s build
what’s next
Start with a Data Discovery Sprint, a structured engagement that produces a data-readiness map, an AWS-first warehouse and pipeline architecture, a quality plan, and a governance design. A real plan before the build.