Data Engineering Services | ETL, Data Warehouse
Finance runs reports from the ERP. Sales uses the CRM. Operations tracks things in the WMS. Customer data is duplicated across three databases with different customer IDs and no agreed definition of what an active customer is. When the CEO asks for a revenue breakdown by customer segment, four people produce four different numbers.
We build data engineering infrastructure that makes your data consistent, accessible, and ready for reporting and AI. ETL pipelines, data warehouses, data lakes, real-time streaming pipelines, and data quality monitoring. The plumbing that makes everything else possible.
Centralized data warehouse that gives every team a single agreed source of truth
ETL/ELT pipelines that move data from source systems into clean, queryable form on a defined schedule
Real-time streaming pipelines for operational data that needs to be current, not yesterday's batch
Data quality monitoring that catches anomalies in the pipeline before they reach reports and decisions
Recent outcomes
POS data sync and AI OCR · Gas station operations (US)
20,000+ transactions processed in a single day
Built an offline-first sync pipeline pulling POS data from 40+ stations into one central platform every 10 minutes, with an AI OCR layer that reads vendor invoices for staff to confirm in seconds.
HIPAA-compliant data infrastructure · Healthcare (US)
20% reduction in clinical decision-making time
Built the data pipeline behind an AI layer added to an existing remote patient monitoring platform, ranking patients by risk and generating insurer-ready summaries automatically.
The problem
When your leadership team asks for the same metric, does every department produce a different number?
How much of your analysts' time is spent cleaning and joining data instead of answering business questions?
Short answer
RaftLabs builds ETL pipelines, data warehouses, and real-time streaming for businesses across the US, UK, Europe, Canada, and the UAE. Per McKinsey's State of AI 2025, 78% of organizations use AI in at least one business function. A focused warehouse covering 3 to 5 source systems costs $30,000 to $80,000 at a fixed price and ships in 8 to 12 weeks.
Key takeaways
Trusted by
The CEO asks for revenue by customer segment. Finance pulls it from the ERP. Sales pulls it from the CRM. Operations reads it off the WMS. A fourth number comes out of a database nobody quite remembers building. Every team is confident. No two numbers match.
Nobody bought bad software. The data is all there, sitting in the ERP, the CRM, the WMS, and a handful of databases. It has just never been connected, cleaned, and made queryable, so every question turns into an argument about whose export is right.
Data engineering removes the argument. One warehouse, one definition of an active customer, one number the whole company can trust.
Every AI project starts with data. Before a model can be trained, the data has to be consistent, complete, and in the right shape. A BI dashboard is only as accurate as the sources feeding it. Analysts can only answer questions when the data sits in one place they can query. Most $1M-$100M businesses already have the data they need. The problem is it has never been unified into a queryable foundation.
According to McKinsey's State of AI 2025, 78% of organizations now use AI in at least one business function. The data underneath that ambition rarely keeps up: Gartner estimates poor data quality costs organizations $12.9 million a year on average (Gartner, 2021). The data usually exists. It is just fragmented across systems that were never connected. RaftLabs has been shipping production software since 2015 for clients across the US, UK, Europe, Canada, and the UAE, including Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. One team scopes the data problem, builds the pipelines and warehouse, and hands it over, with GDPR, HIPAA, and SOC 2 requirements designed in from week one, not retrofitted before launch. For a US gas station operator, we built an offline-first sync pipeline that pulls POS data from 40+ locations into one central platform every 10 minutes. An AI OCR layer reads vendor invoices and presents them to staff for quick confirmation instead of manual re-entry. Data engineering is the work that makes both reporting and AI development possible, and for models grounded in your own knowledge base it is the layer that feeds retrieval-augmented pipelines too.
Zillow's home-buying division is the clearest reported case of a business trusting a pipeline that couldn't keep up. Zillow Offers priced home purchases using an algorithm fed by an internal data pipeline, and as the housing market shifted quickly in 2021, the pricing model reportedly couldn't be trusted fast enough, by the time forecasting errors surfaced, thousands of homes were already under contract at prices Zillow couldn't profitably resell. The company reported a $420 million loss in its home-buying business for Q3 2021, announced it would stop buying homes and wind down roughly 7,000 homes already on its books, and cut 25% of its workforce. Nobody wrote bad code. The pipeline feeding the decision wasn't trustworthy fast enough, and the business found out at the worst possible scale to learn it.
Everything on the left should already be true for your operation. Even one thing on the right, and a configured reporting tool is the smarter first step.
Your data is spread across an ERP, CRM, WMS, and databases that have never been connected into one queryable source.
Different departments produce different numbers for the same metric, and analysts spend more time joining data than answering questions.
You need a foundation for reporting or AI, and budget for a build from $30,000.
What we build
Tell us your source systems, your current reporting pain, and the business decisions you cannot answer from your data today. We will scope a data infrastructure that fixes it.
How it works
Every project follows the same four phases. Scope is locked and price is fixed before development starts.
We map your source systems, identify data quality issues, and document what business questions you need to answer. You leave week 1 with a written scope and a fixed-price quote. No pipelines start without your sign-off.
We design the warehouse schema, data model, and pipeline architecture before writing a line of code. Decisions made here cost ten times less than the same decisions made in week 8.
Pipelines running to a staging warehouse by the end of sprint one. Bi-weekly demos. Data quality checks run in parallel with every sprint, not as a phase at the end.
Production deployment with pipeline monitoring and alerting activated on launch day. 8 weeks of post-launch support included in every project.
What clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
USAAll of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!
01 / 02
Rarely the pitch. Always the difference between a warehouse everyone trusts and one more source of disagreement.
Loading raw data first and transforming it inside the warehouse means you can re-run transformations when a business definition changes without re-extracting from source. Cheap modern warehouse compute made this the default, not a compromise.
A build that technically works but needs someone manually keeping it running every month isn't automation, it's a recurring cost with extra steps. A pipeline that runs unattended is the actual goal.
By the time a wrong number gets caught in a board meeting, the decision built on it may already be made. Completeness, freshness, and schema-drift checks exist to catch the problem upstream, before it costs a decision.
Integration complexity and data quality issues get surfaced in week one specifically so the fixed price holds. Scoping after the fact is how a warehouse project turns into a change-order spiral.
Every project is priced at a fixed cost after a scoping phase that includes a data audit. Where you land depends on the work involved, not negotiation:
The main cost drivers are the number and complexity of source systems, data quality issues in those systems, and whether you need real-time pipelines or batch is sufficient.
What it costs
A written scope and a firm quote for the first phase before any pipelines are built, then working software running to a staging warehouse from the end of sprint one.
The scoping phase includes a data audit and ships a focused warehouse in 8 to 12 weeks. Start with one warehouse, then add real-time pipelines as more source systems come online.
Start with a data audit and one warehouse. Once the pipeline is running against real data, we scope the next phase and expand the infrastructure from there.
No hourly billing
Once we scope your first phase, that price is locked in writing. No hourly billing, no surprise invoices. A scope change is a priced change request, agreed or dropped, never slipped into the final invoice.
Data audit first
The scoping phase includes a data audit that surfaces integration complexity and data quality issues before development starts, so there are no mid-project surprises.
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Read moreETL (Extract, Transform, Load) transforms data before it reaches the destination: data is extracted from source systems, cleaned and shaped in a processing layer, and then loaded into the data warehouse in its final form. ELT (Extract, Load, Transform) loads raw data into the destination first and performs transformations there: data lands in the warehouse in its raw state and is transformed using the warehouse's own compute. ETL made sense when storage was expensive and compute was limited. Modern cloud data warehouses (Snowflake, BigQuery, Redshift) have cheap storage and powerful in-warehouse compute, which makes ELT the default choice for most projects today. ELT preserves the raw data, which means you can re-run transformations when business definitions change without re-extracting from source. It also makes debugging easier because you can see exactly what came out of source systems. We use ELT as the default architecture and recommend ETL only when the raw data is too large, too sensitive, or too costly to store at full volume.
A focused data warehouse project, connecting 3-5 source systems, building core entity models (customer, product, transaction), and delivering a functional analytical layer, typically takes 8-12 weeks. The variables are the number and complexity of source systems, data quality issues in those systems, the number of business logic transformations required, and whether you need real-time pipelines or batch is sufficient. We scope the project based on your specific source systems and target use cases before quoting a timeline. The scoping phase includes a data audit that surfaces integration complexity and data quality issues before development starts, so there are no mid-project surprises.
For most $1M-$100M businesses, Snowflake or BigQuery are the default choices. Both are fully managed, scale elastically, have mature ecosystems of BI tools and data connectors, and have predictable cost at typical query volumes. Snowflake is stronger for workloads that mix structured and semi-structured data and for organizations that want to share data across teams. BigQuery integrates tightly with Google Cloud and is often the natural choice if your data is already in GCP or Google Workspace. Redshift is worth considering if your team is already deep in the AWS ecosystem and wants tight integration with other AWS services. We assess your existing infrastructure, team familiarity, query patterns, and cost expectations and recommend the platform that fits. We do not have a commercial relationship with any platform vendor.
Data quality monitoring watches your data pipelines for anomalies that indicate something has gone wrong upstream. The categories are: completeness (a table that should have 10,000 rows arrived with 4), freshness (data that should update hourly hasn't updated in 14 hours), schema changes (a source system added or renamed a column without telling anyone, breaking downstream transformations), value distribution shifts (a column that always contained values between 0 and 100 now contains values up to 50,000, suggesting a unit change or upstream bug), and referential integrity failures (customer IDs in the transactions table that don't exist in the customers table). Each of these can corrupt reports and AI model inputs silently if they go undetected. We build data quality checks into the pipeline as a first-class deliverable, not an afterthought.
A focused data warehouse project connecting 3-5 source systems with core entity models and a functional analytical layer typically runs $30,000-$80,000. A broader data infrastructure build covering multiple source systems, real-time streaming pipelines, data lake architecture, and data quality monitoring typically runs $80,000-$200,000. The main cost drivers are the number and complexity of source systems, data quality issues in those systems, and whether you need real-time pipelines or batch is sufficient. Every project is priced at a fixed cost after a scoping phase that includes a data audit.
Every AI project starts with a data problem. Before a model can be trained or a RAG pipeline can be grounded in your knowledge base, the underlying data has to be consistent, accessible, and in the right shape. Data engineering is the work that makes AI possible: it connects your source systems, cleans and normalizes the data, and delivers it to the feature store or training pipeline in a form the model can use. RaftLabs handles both the data infrastructure and the AI build, so the two layers are designed to work together from day one. See our AI development services and data engineering for AI.
Work with us
We scope Data Engineering Services in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
