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AI & Data

Data Engineering & Architecture

Build a data foundation that’s engineered for scale and governed for trust.

OnX helps you build pipelines for moving and transforming data, supported by a modern data platform.
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Break through data bottlenecks.

Data streams into the enterprise from every direction, arriving in incompatible formats, trapped in proprietary tools, and siloed across teams. That’s the opposite of what analytics and AI workloads demand: high-quality data that’s unified, governed, and delivered quickly.

Because of data bottlenecks, leadership teams discover problems weeks late. Analysts devote more time to reconciling than producing reports. And every major change makes the gap wider. 

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The OnX approach

Engineering your data pipelines and platform together

Pipelines designed in isolation from the underlying platform get brittle. Platforms modernized without engineering discipline end up empty. That’s why OnX designs them together: the cloud-native lakehouse, warehouse, or hybrid architecture that stores your data and the pipelines that move and transform it.

OnX data engineers and architects have done this work across Microsoft Fabric, Azure, Snowflake, Databricks, and other major cloud analytics platforms. We reduce risk and accelerate time to value with proven patterns like medallion architecture, ELT pipelines, DataOps practices, and governed bronze/silver/gold zones. And because we’re vendor neutral, we always recommend the platform that best meets your business needs. 

Data Engineering & Architecture capabilities

 OnX shapes every data engineering and architecture engagement where the work is needed most.

Data Engineering

Data Engineering

Pipeline Design & Implementation


We design pipelines using ETL, ELT, and streaming patterns appropriate to data source, volume, and analytical use cases. And we build for observability, testability, and recoverability as well as functionality.

Data Engineering

Data Engineering

Data Integration


Unify your data across SaaS platforms, ERPs, financial systems, IoT sources, line-of-business applications, and legacy databases, creating a trusted source of truth for downstream analytics and AI workloads.

Data Engineering

Data Engineering

Medallion Architecture & Data Quality


Organize pipelines into bronze, silver, and gold zones so every downstream consumer pulls from the layer engineered for their use case. We build data quality checks, lineage tracking, and validation into the pipeline from day one.

Data Engineering

Data Engineering

DataOps & Pipeline Operations


We configure the monitoring, alerting, and process controls that keep data flowing reliably after pipelines go live. We can shift into ongoing managed DevOps services, or transfer to your team with the playbooks and runbooks built during implementation.

Data Modernization

Data Modernization

Legacy Platform Migration


Move data from aging on-premises warehouses, siloed file shares, and proprietary platforms to cloud-native architectures. We plan the migration, size the target platform, manage the cutover, and decommission the legacy footprint.

Data Modernization

Data Modernization

Cloud Data Warehouse Implementation


OnX architects evaluate the workload, existing tech stack, and long-term roadmap to recommend the platform that fits. We cover the major platforms, including Microsoft Fabric, Snowflake, Databricks, Azure Synapse, AWS Redshift, and Google BigQuery.

Data Modernization

Data Modernization

Data Lakehouse Architecture


Modern lakehouse architectures combine the flexibility of a data lake with the structure of a warehouse, anchored on Microsoft Fabric OneLake, Databricks, or the cloud-native equivalent.

Data Modernization

Data Modernization

AI-Ready Data Architecture


We help prepare your architecture for what AI workloads demand: high-throughput access patterns, vector storage for RAG and embeddings, governed datasets curated for model training and inference, and integration points for connecting AI applications to the data estate.

Where to start

Advisory engagements

A CBTS advisory is a time-bound, fixed-fee engagement designed to give you a clear answer to a specific strategic question — fast.  

AI & Data Maturity Assessment

Best for organizations that want a clear, third-party read on where they stand on AI and data readiness and where to focus first.

You walk away with: 


  • Current-state assessment across both AI and data dimensions
  • Gap analysis against industry benchmarks and your own stated AI ambitions
  • Prioritized list of foundational gaps to close before scaling AI investment
  • Short-form executive readout deck for leadership alignment
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What success looks like

Three outcomes show up most frequently for the clients we support.

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Operational excellence

With trusted data flowing reliably from source to consumer, leadership stops discovering problems weeks late. Analysts stop reconciling. And the data estate becomes something the business depends on.

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Improved productivity

New data sources onboard in days, not quarters. Analytics workloads that used to run overnight finish in minutes. And the data team’s capacity shifts from plumbing to value.

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Reduced risk

Modern architecture with governed zones, lineage, and quality controls dramatically reduces regulatory, audit, and AI-failure risk.

 “We often see organizations invest heavily in AI pilots that never reach production because the underlying data pipelines aren’t reliable or the platform wasn’t built to support enterprise-scale workloads. Our work happens earlier in the process: building the foundations that turn promising concepts into measurable business outcomes.”

Celio Casadei

 Celio Casadei

 Senior Vice President, Professional Services & AI Consulting

Don’t take our word for it

“OnX has been an incredible partner and really takes the time to understand our needs and our culture. They have been fantastic throughout and represent OnX professionally and with curiosity about our technology landscape.”

DirectorHealthcare

“Onx is exceptionally agile partner, consistently attentive to our needs and always quick to adapt. Their customer focus and responsiveness truly set them apart as a top-tier service provider.”

Deputy CTOBFSI

“OnX is a reliable and trusted partner whose deliberate focus on understanding our environment, challenges, and business outcomes helps us advance complex initiatives with confidence.”

ManagerGovernment

“The OnX account team consistently demonstrates professionalism, expertise, and a strong commitment to service. They translate customer requirements into practical, cost-effective solutions, making them a valuable partner.”

 Sr. ManagerBFSI

“The OnX account team consistently demonstrates professionalism, expertise, and a strong commitment to service. They translate customer requirements into practical, cost-effective solutions, making them a valuable part.”

DirectorUtilities

The data foundation connects everything else.

Data engineering and modernization sit in the middle of the AI and analytics value chain. Strategy sets the direction. Infrastructure provides the platform. Analytics and governance turn the data into trusted, usable insight. Most OnX clients engage across several disciplines as their program matures.

What makes the difference

National expertise with local accountability.

For 40+ years, OnX has helped Canadian organizations solve complex technology challenges. Our national reach provides access to deep technical capabilities, industry specialists, and leading technology partners, while our local teams remain accountable for outcomes and invested in your success. We listen before we recommend and stay engaged throughout delivery.

Industry knowledge that matters. 

Regulatory requirements and operational realities shape your technology decisions. OnX brings deep experience supporting complex, highly regulated organizations through modernization, cybersecurity, cloud transformation, and AI adoption. With a deep understanding of governance, compliance, and security, we know how to deliver outcomes within those constraints.

Partnership that goes the distance.

Technology initiatives succeed when the right partner stays committed after implementation. OnX works alongside you from strategic planning and procurement to modernization, managed services, and AI transformation. We strive for partnerships built on trust, accountability, and a shared commitment to long-term success.

Further reading on IT modernization

Perspectives from OnX experts on modernizing the foundation your business runs on.

Frequently asked questions 

What is data engineering, and how is it different from data modernization? Data engineering is the discipline of designing, building, and operating the pipelines that move and transform data — ingestion, ETL or ELT processing, integration, quality controls, and ongoing operations. Data modernization is the work of replatforming the data estate itself, moving from legacy on-premises warehouses, siloed file shares, or aging proprietary tools onto a cloud-native architecture like a lakehouse or modern cloud data warehouse. The two are usually needed together. Pipelines without a modern platform stay brittle, while a modern platform without engineering discipline ends up empty or unreliable.
What’s the difference between a data lake, data warehouse, and lakehouse? A data lake stores raw, unstructured data in its original format; it’s flexible, but harder to query directly. A data warehouse stores structured data processed and modeled for analytics; it’s easier to query but rigid and expensive to expand. A lakehouse combines both. You get lake-style flexibility for raw and semi-structured data with warehouse-style structure and performance for the curated layers on top. Most modern enterprise data architectures are now lakehouse-based, often built on Microsoft Fabric, Databricks, or Snowflake, with bronze/silver/gold zones layered inside.
What’s the difference between ETL and ELT? Both move data from a source into a storage location. ETL (extract, transform, load) transforms the data before loading it into the destination, typically because the destination is a structured warehouse that needs clean data on arrival. ELT (extract, load, transform) loads raw data first and transforms it inside the destination, which works well with cloud-native lakehouses and modern data warehouses that handle transformation at scale. Most new pipelines are designed ELT, while many legacy environments still run ETL. OnX designs to fit the platform and the use case, not to favor one pattern.
How does data affect the success or failure of AI projects? Industry research puts the AI project failure rate above 70%, and most of those failures trace back to data that’s incomplete, inconsistent, or inaccessible. It lacks the quality, lineage, or governance the model needs to be trusted. Data engineering and modernization solve this by building the pipelines and platform that deliver AI-ready data. Such data is integrated across sources, validated for quality, governed by zone, and accessible at the throughput AI workloads demand.
Which platforms does OnX work with? OnX is vendor neutral and platform certified across the major cloud and data ecosystems, including Microsoft Fabric and Azure, Databricks, Snowflake, AWS (Redshift, S3, Glue), and Google Cloud (BigQuery, Dataflow). Most of our recent enterprise engagements have centered on Microsoft Fabric, Snowflake, and Databricks, but the recommendation is driven by your existing technology stack, use cases, and long-term roadmap.

Start with a conversation.

Your organization’s AI ambitions depend on data engineered and modernized to support the work.