Data Engineering & Platform Modernization Services
Building the data infrastructure and pipelines that modern analytics and AI depend on.
Our Data Engineering Approach
Data engineering services create the pipelines, platforms, and data models that move raw data from source systems into reliable, structured forms that analytics and AI can depend on.
Our approach focuses on building data infrastructure that is maintainable, scalable, and aligned with how your teams actually work. We modernize legacy platforms incrementally, reducing disruption while improving the quality and reliability of your data foundation.
What We Focus On:
- Designing and building reliable data pipeline development from source to consumption
- Performing data platform modernization on legacy warehouses and on-premise systems
- Building data models that support analytics and AI through data lakehouse architecture
- Ensuring data is accurate, consistent, and available when needed
- Implementing DataOps practices to reduce fragile, one-off integrations
Build a Data Foundation That Works
Data Pipeline Design & Development
We design and build data pipeline development solutions that move, transform, and load data from source systems into target platforms reliably and at scale.
What’s Included:Â
Ingestion pipelines for operational systems, APIs, and streaming sources (ETL and data integration)
Data transformation and business logic implementation
Batch and real-time pipeline development
Pipeline monitoring, alerting, and error handling
Data Platform Architecture
We design and implement modern cloud data platform architectures including data lakehouse architecture that support the scale and performance your workloads require.
What’s Included:Â
Data lakehouse architecture design and implementation
Cloud data platform on Azure, AWS, and GCP
Platform selection and technology evaluation
Security and access control design
Legacy Data Platform Modernization
We help organizations perform data platform modernization on aging data warehouses and on-premise platforms to support modern analytics and AI without rebuilding from scratch.
What’s Included:Â
Assessment of current platform and technical debt
Data warehouse migration planning and execution
On-premise to cloud migration
Performance and scalability improvements
Data Modeling & Semantic Layer
We build well-structured data models and semantic layers that make data consistent and reusable — the foundation of strong data engineering services.
What’s Included:Â
Dimensional and entity data modeling
Semantic and metrics layer development
Data model documentation and governance
Support for self-service analytics
DataOps & Platform Enablement
We implement DataOps processes and tooling that allow data engineering services to be developed, tested, and deployed reliably and at speed.
What’s Included:Â
CI/CD pipelines for DataOps workflows
Testing frameworks for data pipelines
Observability and monitoring for data platforms
Team enablement and engineering standards
Trusted by Leading Organizations
Case Studies
See Our Delivery Framework
See how we deliver real results with a proven model that brings clarity, speed, and confidence to every project.
AI Is Big. Your First Step Doesn’t Have to Be.
Download our free AI strategy guide and take the guesswork out of getting started.
FAQs
Data engineering services involve designing and building the systems that collect, move, transform, and store data so it can be used by analytics and AI — including pipelines, platforms, data models, and the processes that keep data reliable.
AI models and analytics tools are only as good as the data they consume. Without reliable data pipeline development and clean, structured data, analytics are inaccurate and AI projects fail. Data engineering services create the foundation everything else depends on.
A data warehouse stores structured data optimized for reporting. A data lakehouse architecture combines the scalability of a data lake with the structure of a data warehouse, supporting both analytics and AI workloads from a single cloud data platform.
We build cloud data platform environments on Microsoft Azure, Amazon Web Services, and Google Cloud Platform, selecting the right platform based on existing infrastructure and long-term requirements.
Yes. We approach data platform modernization incrementally, assessing your current environment and identifying the changes that deliver the most value with the least disruption. Data warehouse migration rarely requires a full rebuild.
DataOps applies software engineering practices such as version control, automated testing, and CI/CD to data pipeline development. It allows data teams to build, test, and deploy pipelines faster and with fewer errors.
We implement monitoring, alerting, and testing as part of every data pipeline development project. We also establish DataOps standards that allow pipelines to be maintained without introducing new risk.
Yes. We design and build both batch and real-time data pipeline development solutions. Real-time pipelines are appropriate for operational dashboards, alerting, or real-time AI scoring.
Data quality is built into our data engineering services design, not added afterward. We implement validation, testing, and monitoring at key points so issues are caught before they reach analytics or AI systems.
A semantic layer sits between your data and analytics tools, defining consistent business metrics. It is a critical component of strong data engineering services ensuring every metric means the same thing everywhere.
We’re Ready When You Are
Whether you have a clear goal, a rough idea, or a problem that needs fixing, we’re here for it.
Fill out the form and we’ll follow up to schedule a short, focused conversation. We’ll walk through what you’re trying to achieve, share how we work, and outline the next steps to move forward with confidence.
Address
802 N. Pinyon Ct,
Hartland, WI 53029
Billing Inquiries
(866) 568-8615
Information and Sales
info@xorbix.com