Data Stuck in Silos
CRMs, ERPs, spreadsheets and apps each hold part of the picture.
Data Engineering Services That Turn Scattered Data Into One Reliable Source
We build the pipelines and integrations that bring data from every system into one clean, trusted place, so your analytics, reports and AI run on numbers everyone can rely on. We handle batch and real-time data, document every pipeline and add monitoring so problems surface early.
Challenges
Why Data Is
Hard to Use
Most companies have plenty of data. The problem is that it lives in too many places and nobody trusts it.
Reports Nobody Trusts
Different teams pull different numbers for the same question.
Fragile, Manual Pipelines
Scripts and manual exports break, and someone has to fix them by hand.
Not Ready for AI
AI projects stall because the data behind them is incomplete or messy.
Book a call
Talk to a Data Engineer
Talk to a Data Engineer
Service
Our Data Engineering Services
As a data engineering company, we design, build and run the data foundation your business depends on.
Data Pipeline Development
Automated pipelines that move data reliably from source systems to your warehouse.
Data Integration Services
Connect CRMs, ERPs, databases, APIs and SaaS tools into one data flow.
ETL Development Services
Extract, transform and load processes that clean and standardize data as it moves.
Modern Data Stack Consulting
Choose and set up the right warehouse, pipeline and transformation tools for your size.
Data Engineering Consulting Services
Assess your current data setup and get a clear plan to fix and scale it.
Data Engineering Outsourcing
A dedicated team that builds and maintains your pipelines, so your staff can focus on insights.
Process
How We Build Your Data Foundation
A three-phase approach from messy sources to reliable, automated data.
Phase 1: Map the Data Landscape
Timeline: 1-2 weeks
We map your data sources, quality issues and reporting needs, then design the target architecture.
Deliverables:
- Data source inventory
- Data quality assessment
- Target data architecture
- Tool recommendations
- Delivery roadmap
Phase 2: Build the Pipelines
Timeline: 3–7 Weeks
We build automated pipelines, integrations and data models, with tests at every step.
Deliverables:
- Automated data pipelines
- System integrations
- Clean, modeled data sets
- Data quality checks
- Documentation
Phase 3: Operate & Scale
Timeline: 8 weeks and ongoing
We monitor pipelines, fix issues before they reach reports and add new sources as you grow.
Deliverables:
- Pipeline monitoring and alerts
- Issue resolution
- New source onboarding
- Performance and cost tuning
- Monthly data health reports
Benefits
Build a Stronger Data Foundation
One Source of Truth
Every team works from the same trusted numbers.
Less Manual Work
Automated pipelines replace exports and copy-paste.
Faster Reporting
Fresh data arrives on time, every time.
Ready for AI
Clean, connected data that AI and machine learning can use.
Room to Grow
Add new systems and data sources without starting over. p.
Use Cases
Built for the Data Challenges of Your Industry
Data Engineering for Healthcare
Connected patient and operational data We integrate EHR, lab and operational data with privacy controls built in.
EHR and lab system integration- FHIR and HL7 data pipelines
- De-identified data for analytics
- Audit-ready data access
Case Studies
Proven Builds, Not Just Prototypes
Projects Of Novatore Solutions
ReactNode.js
OneH2
OneH2 needed a logistics platform to manage fleet and supply chain operations for hydrogen fuel transportation across the US market. Novatore built the solution on React and Node.js, giving operations teams a single, continuously available view of moving assets and delivery performance across the network.
Fleet Visibility
40–50% better
Challenge: Managing fleets, deliveries, and 24/7 hydrogen fuel demand requires teams to track constantly changing operational data. Without centralized, real-time visibility, it becomes difficult to monitor asset movement, delivery performance, and supply-chain operations efficiently.
Solution: Novatore contributed to a React and Node.js platform that brought fleet, supply-chain, and delivery data into a unified operational view. Real-time dashboards gave teams a clearer picture of moving assets, delivery activity, and overall logistics performance.
Result: Operations teams gained a shared, up-to-date view of fleet and delivery performance, making it easier to monitor assets, identify operational issues, and respond to changing logistics demands.
Read Case Study
Demand-Aware EV ChargingInfrastructure for the Pakistani Market
Permit Place
Permit Place needed a way to bring due diligence, permit packet submissions, and fee calculations into one connected system for the construction managers and real estate professionals it serves. Novatore built the platform on React and Laravel, replacing scattered files and email threads with a single consolidated workflow.
Document Processing Time
35–45% reduction
Challenge: Due diligence, permit packet submissions, and fee calculations relied on scattered files, making real estate workflows slow to track and manage.
Solution: Novatore supported a React and Laravel platform connecting document and data workflows, bringing due diligence, permit submissions, and fee calculations into one system.
Result: The firm now manages due diligence, permit submissions, and fee calculations from one connected system instead of scattered files.
Read Case StudyTools & Technologies
Technologies We Build With
We use proven, production-grade tools and choose the right ones for each project based on performance, cost and security.
FAQs
Data Engineering FAQs
What teams usually ask before investing in data engineering.
A data engineering company designs and builds the systems that collect, move, clean and store your data. This includes data pipelines, integrations between business systems, data warehouses and quality checks. The result is reliable, analysis-ready data that powers reports, dashboards and AI.
ETL (extract, transform, load) cleans and transforms data before loading it into a warehouse. ELT (extract, load, transform) loads raw data first and transforms it inside the warehouse. ELT is common with modern cloud warehouses like Snowflake and BigQuery because they can process large data volumes quickly and cheaply.
Data engineering services typically cost $25 to $49 per hour, depending on the number of data sources, data volume and complexity. Connecting a few systems to a warehouse sits at the lower end, while enterprise platforms with many sources and real-time pipelines cost more. We give a clear estimate after a free discovery call.
Yes. We connect to your current CRMs, ERPs, databases and SaaS tools through APIs and connectors.
Yes. We monitor and maintain pipelines and add new data sources as your business grows.
Yes. Data is encrypted in transit and at rest, with access limited to what each system needs.
Your Go To Business Partner
Why Novatore
An AI-Native technology partner
We combine strategy and delivery to build what actually ships, from roadmap to production.
Teams Which Deliver
Strategists, engineers, and QA specialists work as one team to turn plans into software fast.
Full-stack technical depth
React, Next.js, Node.js, Flutter, and Laravel expertise, paired with modern AI and cloud tooling, covers the full delivery lifecycle.
US, UK, and Canadian Clients
We work inside the timelines, communication norms, and compliance expectations our North American and UK clients require.
Backed by 50+ shipped platforms
50+ products delivered from concept to production across HealthTech, FinTech, retail, and SaaS, with proven experience.
We Are Just One Click Away
Build a Data Foundation You Can Trust
Tell us where your data lives today. We'll show you how to bring it together, reliably.
