Data engineering that ends
the morning spreadsheet ritual

Data engineering is building the pipelines and storage that get data from your source systems into one place, clean and queryable. Analytics is what you do with it once it is there: dashboards, reports, and metrics. Most reporting problems are actually data engineering problems in disguise. The dashboard is slow or wrong because the data feeding it is scattered across systems, inconsistent, or assembled by hand every morning. We fix the pipeline first, then the reporting sits on solid ground.

For many businesses, a well-structured reporting database alongside your operational systems is enough, and a full warehouse platform is overkill. A warehouse earns its keep when you have several source systems, meaningful data volume, and analytics that would slow down production if they ran against it directly. During scoping we look at your actual sources, volumes, and reporting needs and recommend the smallest thing that works: often Postgres with good modelling, sometimes BigQuery or Snowflake, occasionally a lakehouse.

Usually, yes. We have pulled data from systems via APIs, database replicas, scheduled exports, webhooks, and, where nothing else exists, screen-level integration. The approach depends on what the source system allows and how fresh the data needs to be. We assess each source during scoping and tell you honestly which are straightforward and which will need a workaround, so the integration effort is priced accurately rather than discovered later.

Every pipeline we build includes validation and reconciliation as first-class steps, not an afterthought. We profile the source data early to find the duplicates, the inconsistent formats, and the gaps, then build cleaning and deduplication into the pipeline with checks that flag when something looks wrong. For a one-off cleanup of historical data, we scope that as its own piece of work with a clear before-and-after.

A clean, well-modelled data layer is the prerequisite for reliable AI. Retrieval-augmented generation, agents that query your data, and any LLM feature grounded in your own records all depend on the data being consistent and accessible. When we build the pipeline, we design it so a vector store or a retrieval endpoint can sit on top without a nightly export job. Several teams come to us for data engineering specifically because their AI pilot stalled on messy data.

A focused project connecting two or three sources into a reporting database with a set of dashboards typically takes 8 to 14 weeks. A broader platform with many sources, a warehouse, transformation layers, and self-serve analytics runs 16 to 28 weeks. We deliver in phases, so the first useful dashboards land well before the whole platform is finished.

It depends on the number of source systems, how cooperative each one is, the data volume, and whether you need a full warehouse or a reporting database. We scope and fix-price it after seeing your sources. We do not quote a data project without understanding what we are actually integrating, because the source systems are where the cost lives.

You do, completely. Pipeline code, transformation logic, data models, dashboard definitions, and infrastructure configuration transfer to you. We build on standard tools and document the lineage, so your team or another vendor can maintain and extend it without us.

Yes. Most of our clients are in the United States, United Kingdom, and Australia, with agreed real-time overlap hours for each region. For UK and EU work we design pipelines with GDPR in mind, including where data is stored and how personal data is minimised in analytics.

The reporting infrastructure in COMPASS for Ball State University follows the model we use: source data collected under clinical research standards, transformed into a structure researchers can query and report against. i-mve consolidates job, quoting, invoicing, and accounting data across integrations into one operational view for 510 companies. We have built ETL between systems, reporting databases, and real-time dashboards across logistics, healthcare research, and property since 2017.