Data Engineering Services

Data Engineering Services That Build Pipelines and Warehouses for Analytics and AI

Data Engineering

Data Pipelines, Warehouses, Models, Priced Before We Build

Five services that turn scattered systems into one dependable data layer: pipelines and warehouses, quality and cleansing, analytics, visualization, and data science. Each one is scoped and priced before work starts, built in your own cloud, and handed over with schemas, lineage and a runbook.

Book a 30-minute scoping call See the five services

60%Of AI projects without AI-ready data will be abandoned through 2026, says Gartner

Your cloudPipelines, code and credentials stay in your own accounts

LineageEvery field traced from source system to dashboard, in writing

Fixed priceScope and price agreed in writing before any work starts

Built in your own cloud, warehouse and repositories · NDA and DPA signed before any data moves · Delivered from Dhaka, Bangladesh on UTC+6

Which data services does EICRA offer?

Five services, listed in the order the work actually runs, plus a dedicated team for when the backlog never ends. Each is a separate engagement with its own scope and price, and each one feeds the next.

Data Engineering: AI-Ready Data

Pipelines, warehouses and modelled tables that feed reporting and AI, with schemas, scheduling, monitoring and documented lineage your team can read.

Data Quality and Cleansing

Deduplication, standardisation, validation rules and a quality scorecard, so the same customer is one record in every system.

Data Analytics

Metric definitions, cohort and funnel analysis, and reporting built on modelled tables rather than on exported spreadsheets.

Data Visualization

Dashboards built around the decision they support, with defined metrics, sensible defaults and no chart that nobody uses.

Data Science and Machine Learning

Forecasting, scoring, segmentation and recommendation models trained on your own data, with measured accuracy and a retraining plan.

Dedicated Data Engineering Team

Named engineers working continuously on your data backlog, inside your own cloud, repositories and ticketing, priced per engineer per month.

Which data service do I need?

Start from what is broken. If numbers disagree between systems, fix quality first. If reporting means exporting spreadsheets, you need pipelines. If nobody opens the dashboards, fix visualization. If you want a prediction, you need data science — but only after the first two.

Find your situation on the left, then read across
Your situation	What you need	Where we start
The same figure is different in two systems	Data Quality and Cleansing	Duplicate and rule audit
Reports are built by exporting spreadsheets by hand	Data Engineering: AI-Ready Data	Pipeline design
An AI project stalled once it met your real data	Data Engineering: AI-Ready Data	Readiness assessment
Every team defines the same metric differently	Data Analytics	Metric dictionary
Dashboards exist but almost nobody opens them	Data Visualization	Decision review
You want a forecast, a score or a segment	Data Science and ML	Baseline model

Two-minute fit check: which data service suits you?

Five questions. Read across your answer and note the service named. Whichever service appears most often is where to start — and if two tie, take the one furthest upstream, because everything downstream gets rebuilt when it changes.

Answer each question, then count which service appears most
Question	Option A	Option B	Option C
1. Where does your reporting data come from?	A modelled warehouse → Data Analytics	Exports and spreadsheets → Data Engineering: AI-Ready Data	Straight from production systems → Data Engineering: AI-Ready Data
2. Do two systems ever disagree on one figure?	Rarely, and we know why → Data Analytics	Often; we reconcile by hand → Data Quality and Cleansing	We stopped checking → Data Quality and Cleansing
3. Is every key metric defined the same way?	Yes, and it is written down → Data Science and ML	Roughly, but not written → Data Analytics	Every team differs → Data Analytics
4. Who opens the dashboards each week?	Named owners, every week → Data Science and ML	A few people, sometimes → Data Visualization	Almost nobody → Data Visualization
5. What should the data do next?	Explain what happened → Data Analytics	Be seen clearly by others → Data Visualization	Predict what happens next → Data Science and ML

Five possible outcomes: Data Engineering: AI-Ready Data · Data Quality and Cleansing · Data Analytics · Data Visualization · Data Science and ML. If two tie, start with the one furthest upstream — a dashboard or a model built on unready data has to be rebuilt the moment the pipeline beneath it is fixed.

In what order should data engineering work be done?

Three rules in plain language. We apply them in this order, and we say so when the cheaper step upstream is the one you actually need.

Rule 1: build the data pipeline before the dashboard

A dashboard built on a hand-made export is a screenshot with a refresh button. It breaks the first time someone leaves, a column is renamed, or the file is not updated on a Monday.

So the pipeline comes first: scheduled loads, validation on every run, alerts to a named owner, and a modelled layer everything else reads from.

Rule 2: agree metric definitions before you build dashboards

Most reporting arguments are not about the chart. They are about whether revenue includes refunds, whether a user is active at seven days or thirty, and which date a deal is counted on.

A written metric dictionary with an owner per metric settles that once. Without it, every new dashboard restarts the same argument.

Rule 3: prove the data is AI-ready before you fund a model

Models are usually not the reason an AI project dies. The demo runs on a clean extract someone prepared by hand, and the build fails later against the systems the business actually runs.

So readiness is assessed against the specific use case first, in writing, before anyone commits a model budget.

AI-ready is not the same as BI-ready: a warehouse that answers last quarter’s questions can still be unusable for a model. AI needs history deep enough to learn from, labels, metadata, and permissions that cover the new purpose. Working dashboards are evidence that reporting works, not that the data is ready.

Why choose EICRA for data engineering?

A Gartner survey of data management leaders found that 63% of organizations either do not have, or are unsure they have, the right data management practices for AI, and Gartner predicts that through 2026 organizations will abandon 60% of AI projects unsupported by AI-ready data (Gartner, 26 February 2025). Pipelines are the cheap part; readiness is the part that decides.

What a typical low-cost data build leaves out, and what we include
Item	Without	With EICRA
Source of truth	Whichever export was most recent	One modelled layer every report and model reads from
Metric definitions	Different in each team’s spreadsheet	A written dictionary with a named owner for each metric
Data quality	Noticed when a number looks odd	Validation rules on every load, with a quality scorecard
Pipeline failures	Found by whoever reads the report	Alerts to a named owner, with retries and a documented fallback
Lineage	Nobody can say where a figure came from	Every field traced from source system to dashboard in writing
AI readiness	Assumed, because the dashboards work	Assessed against the use case before a model is funded
Ownership	The pipeline lives on one laptop	Code in your repository, jobs in your cloud, runbook included
Contracts	A freelancer invoice	Company contract with NDA, DPA and GDPR Article 28(3) terms

Data engineering FAQs: cost, tools, AI-ready data and ownership

Cost is at the top, because that is the question everyone opens with.

How much do data engineering services cost?

Every data engagement is fixed-scope and fixed-price, quoted after a 30-minute scoping call once the sources and the target are known. Cloud, warehouse and tool subscriptions are billed to you directly by the provider. Our published rates and payment terms sit on the pricing page.

What is AI-ready data, and how is it different from BI-ready data?

BI-ready data answers a known question on agreed dimensions. AI-ready data must also carry the history, the labels, the metadata and the permissions a model needs, and stay consistent as it drifts. Dashboards working is not evidence that the data is ready.

Which data service should I start with?

Whatever sits furthest upstream of your problem. Quality and pipelines come first, because analytics, dashboards and models all get rebuilt when those change. Start with analytics or visualization only if your data already lands in a modelled warehouse you trust.

Do you work in our cloud or yours?

Yours. Pipelines run in your own cloud account, code sits in your repository and credentials stay with you. Our access is named, least-privilege and removed at handover, and you receive the code, the schemas and a runbook as files you keep.

Which data tools do you work with?

Whatever you already pay for. We build with your existing warehouse, orchestration and BI tools rather than moving you to something we prefer, and we say plainly at scoping when a current tool will not carry the load and what switching would cost.

Can you fix our data without replacing our systems?

Usually yes. Most problems are missing definitions, missing validation and missing ownership rather than the wrong database. We assess first and say so when the honest answer is that a system has to change, instead of billing months against a design that cannot work.

How do you handle personal data in pipelines?

NDA and DPA first, then masked or synthetic data in every non-production environment. Retention periods, the processing purpose and the deletion date are written into the contract, and access stays named and least-privilege throughout the engagement.

Do you support the pipeline after handover?

Optionally. Every build ships with monitoring, alerts and a runbook so your own team can run it. Ongoing support is a separate monthly agreement with an agreed response time, and you are never required to buy it to keep the build working.

Send us one report your team rebuilds by hand every month. We will tell you what it would take to stop.

Book a 30-minute scoping call