AI Agents & Data
Data engineering services that make your numbers agree
We build the pipelines and warehouse behind your reports and AI projects, so finance, sales and operations stop arguing about whose spreadsheet is right.
- 01Data pipelines and ingestion
- 02Warehouse and lakehouse design
- 03dbt modeling
- 04Data quality and observability
- 05Dynamics 365 and ERP data
- 06Legacy migration
- 07Data for AI projects
At a glance
Data engineering is building the pipelines and warehouse that pull data from your ERP, CRM and SaaS tools into one cleaned, tested place for reporting and AI. It's for companies whose departments report different numbers for the same metric. Tag4 Technology Solutions builds on Snowflake, Databricks or Microsoft Fabric, writes business logic once in dbt and monitors every pipeline.
Updated
- Best for
- Companies with data spread across an ERP, a CRM and several SaaS tools
- Typical timeline
- 8–12 weeks for a first warehouse, 4–9 months for broader programs
- Typical platform cost
- A few hundred to a few thousand dollars a month for a mid-sized company
- Core technologies
- Snowflake, Databricks, Microsoft Fabric, dbt, Airflow, Fivetran
- What you get
- Tested pipelines, a documented warehouse, freshness alerts and runbooks
If three departments report three different revenue figures for last month, you have a data engineering problem, not a reporting one. Our data engineering services are for companies whose data lives in an ERP, a CRM, a few SaaS tools and a shared drive full of exports. You want one reliable place where it all lands, cleaned and modeled, ready for dashboards, forecasting or AI.
Where these projects go sideways is familiar. Someone builds pipelines with a drag-and-drop tool, they break quietly when a source field is renamed, and nobody notices until the board deck is wrong. Business logic ends up scattered across dozens of SQL views, Power BI measures and Excel formulas, so nobody can say which definition of active customer is correct. Teams also over-buy. A company with 50 GB of data does not need a streaming Spark cluster, and its cloud bill will say so.
We size the platform to your data and your team. For many mid-sized companies on Microsoft 365 and Dynamics, Microsoft Fabric is a sensible home. Teams with heavy data science work often do better on Databricks, and Snowflake remains a strong choice for SQL-first analytics across clouds. Whichever you pick, transformations live in dbt or version-controlled notebooks with tests, documentation and code review. Every pipeline has freshness and volume checks, and failures alert a person instead of silently producing stale numbers.
A first working warehouse covering two or three core sources usually takes 8–12 weeks. Broader programs that bring in a dozen systems and retire legacy reports run 4–9 months. Platform costs for a mid-sized company often land between a few hundred and a few thousand dollars a month, and we design with the bill in mind from day one.
What we do
Data Engineering services
Data pipelines and ingestion
Batch and incremental loads from ERPs, CRMs, databases, APIs and files, with change data capture where the source supports it and retries when it hiccups.
Warehouse and lakehouse design
Snowflake, Databricks or Microsoft Fabric set up with sensible layers (raw, cleaned, business-ready), access roles and cost guardrails.
dbt modeling
Business definitions written once as tested SQL models, documented and reviewed like application code, so revenue means the same thing in every report.
Data quality and observability
Freshness, volume, schema and null checks on every critical table, with alerts routed to the person who can fix the source.
Dynamics 365 and ERP data
Getting Business Central, Finance & Operations and Dataverse data into analytics through Synapse Link, Fabric Link or APIs without hammering the live system.
Legacy migration
Moving off aging SQL Server warehouses, SSIS packages or Hadoop onto a modern platform, running old and new in parallel until the numbers match.
Data for AI projects
Feature tables for machine learning and clean, chunked document stores for generative AI, fed by the same governed pipelines as your reports.
How we work
What you can expect from us
Right-sized platforms
We recommend the smallest setup that handles your volume and growth. Fancy architecture you have to pay someone to babysit helps nobody.
Logic in one place
Metric definitions live in the transformation layer, not in individual dashboards. Change it once and every report updates.
Tests on every model
Pipelines ship with automated tests and run through CI before they touch production tables, the same discipline we apply to application code.
Handover your team can run
We document lineage, write runbooks and pair with your analysts, so you aren't stuck calling us every time a new column appears.
Tools and platforms
- Snowflake
- Databricks
- Microsoft Fabric
- dbt
- Apache Airflow
- Dagster
- Fivetran
- Airbyte
- Apache Spark
- Delta Lake
- Apache Iceberg
- Azure Data Factory
- Apache Kafka
- SQL Server
Process
From first call to production
Discovery
A few working sessions to pin down goals, users, constraints and what "done" means. You get a written scope and estimate.
Design
Architecture, data model and clickable UI before heavy coding starts, so surprises show up early and cheaply.
Build
Two-week sprints with a demo at the end of each one. You see working software, not status reports.
Launch
Automated tests, staged rollout, monitoring and a rollback plan. Go-live should be boring.
Support
Bug fixes, updates and new features on a monthly plan, or a clean handover to your team.
FAQ
Data Engineering: common questions
Should we choose Snowflake, Databricks or Microsoft Fabric?
If you're a Microsoft shop using Power BI and Dynamics, Fabric is usually the simplest path. Databricks suits teams doing serious data science and Python work. Snowflake is excellent for SQL-heavy analytics and multi-cloud setups. We'll compare them on your actual workloads before you sign anything.
What is the difference between a data warehouse and a lakehouse?
A warehouse stores structured, modeled tables for SQL and reporting. A lakehouse keeps data in open file formats like Delta or Iceberg on cheap storage, then adds warehouse features on top, which makes it handy when you also have files, logs or machine learning workloads. For many companies the practical difference is smaller than vendors suggest.
Do we need a full-time data engineer after the project?
Not always. A well-built platform for a mid-sized company can often be maintained by a technical analyst, with occasional help for new sources. We can also stay on for a small monthly retainer covering monitoring and changes.
How long does it take to build a data warehouse?
A first version with two or three sources and a handful of core reports usually takes 8–12 weeks. Adding more systems and retiring old reports happens in phases after that. We aim to put useful numbers in front of people within the first month.
Can you work with real-time data?
Yes, using Kafka, Event Hubs or change data capture streams. That said, most business reporting is fine with data refreshed every 15 minutes to an hour, which is far cheaper. We'll tell you plainly if true real-time is worth the cost for your use case.
Related
Often paired with
Data Analytics & Power BI
Power BI dashboards, KPI design and self-service reporting on a data model you can trust.
Learn moreAI & Machine Learning
Predictive models, forecasting and computer vision, deployed with the monitoring to keep them honest.
Learn moreCloud Solutions
Migrations to AWS, Azure and Google Cloud, planned workload by workload with cost built into the design.
Learn moreIndustries we build for
Planning a data engineering project?
Tell us what you are trying to build. An engineer reads every message and replies personally. No sales sequences, no pressure.