Data Engineering Services

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

Data Pipelines, Warehouses, Models, Priced Before We Build

As a Dhaka-based company, our data engineering services turn scattered systems into one dependable data layer for international companies: 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

2 weeksData engineering diagnostic, $1,500, half credited to the pilot

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 · Confidentiality and data processing agreements signed before any data moves · Overlap hours agreed at kickoff

Which data engineering services do we offer?

Our data engineering services are five engagements listed in the order the work runs, plus a dedicated team for a backlog that never ends. Each has its own scope and price, and each one feeds the next: clean, modelled data first, then the reports, dashboards and models that read from it.

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.

How does each data engineering service build on the one below?

Each data engineering service builds on the layer below it. Pipelines load your systems into one modelled warehouse, quality rules make each record trustworthy, analytics fixes the metric definitions, and dashboards and models read the same tables. Start higher up and the upper layers are rebuilt as soon as the lower ones change.

One data layer, built from the bottom up

One trusted source for every report, dashboard and model

Dedicated Data Engineering TeamNamed engineers across every layer

01Pipelines and warehouseScheduled loads from your systems into modelled tables in your own cloud, with alerts to a named ownerData Engineering: AI-Ready Data
02Trusted recordsValidation rules on every load, deduplication and a quality scorecardData Quality and Cleansing
03Agreed metricsA written metric dictionary, with a named owner who signs each definitionData Analytics
04DashboardsOne dashboard per decision, built on the agreed metricsData Visualization
05PredictionsForecasts, scores and segments trained on the same tables, with measured accuracyData Science and Machine Learning

LineageEvery field traced from source system to dashboard

Your systemsProduction databasesSpreadsheets and exportsEvery other source system

Fix the lowest broken layer first: a dashboard or model on unready data is rebuilt once the pipeline beneath it changes.

How to read it: start at the bottom. Each layer reads only from the one beneath it, so a fix below reaches every report and model above, and yellow marks the definitions your own team signs.

Which data engineering service do I need?

The data engineering service you need depends on what is broken. If numbers disagree between systems, fix data quality first. If reporting means exporting spreadsheets, you need pipelines. If nobody opens the dashboards, fix data 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 Machine Learning	Baseline model

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

The fit check is five questions about where your data stands today. Read across each answer and note the service it names; the service named most often is your starting point. If two tie, take the one furthest upstream, because everything downstream is 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 Machine Learning	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 Machine Learning	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 Machine Learning

Five possible outcomes: Data Engineering: AI-Ready Data · Data Quality and Cleansing · Data Analytics · Data Visualization · Data Science and Machine Learning. 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.

How does data engineering work, and in what order?

Data engineering work runs in one order: pipelines before dashboards, definitions before charts, and readiness before any model. We apply these three rules in that order and say so when the cheaper step upstream is the one you actually need. Skipping one usually means rebuilding the work that sits on top of it.

Rule 01: 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 02: 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 03: 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 report-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. The World Wide Web Consortium (W3C) provenance standard (PROV) states that provenance is “information about entities, activities, and people involved in producing a piece of data or thing”, which is why every field carries its lineage. Illustrative example: a retailer’s twelve monthly sales exports become one scheduled pipeline, and the forecast model reads the same tables as the dashboard.

Why choose us for data engineering?

Data engineering is judged by whether the numbers still agree a year after handover, so we build validation, lineage and alerts into every pipeline. Personal data stays “accurate and, where necessary, kept up to date”, as Article 5(1)(d) of the General Data Protection Regulation (GDPR) requires. Reviewed By Eicra.com team

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 and the Article 10 data criteria of the EU AI Act 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 a non-disclosure agreement (NDA), a data processing agreement (DPA) and GDPR Article 28(3) terms

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

Pricing comes first; ownership and timing follow.

How much do data engineering services cost?

Every engagement is fixed-scope and fixed-price. The two-week data engineering diagnostic and its price are shown at the top of this page, with half credited to the pilot, and builds are quoted in writing after it. Cloud, warehouse and tool subscriptions are billed to you directly by their providers.

What do data engineering services include?

Data engineering services, also sold as data engineering as a service, include the pipelines that move data out of your systems, the warehouse and modelled tables that store it, validation rules, lineage and monitoring for AI-ready data. Data engineers design, build and run that layer, so reports, dashboards and models read one trusted source.

Is AI replacing data engineers, or just their routine work?

No. AI tools now draft pipeline code, tests and documentation, which speeds routine work, but someone still has to decide what each field means, who owns it and when a number is wrong. That judgement is the data engineer’s job, and it is why every pipeline we build has a named owner.

Who owns the pipelines, code and data?

You do. Pipelines run in your own cloud account and code sits in your repository. As a Bangladesh-based team we work through named, least-privilege access that is removed at handover, and you keep the code, the schemas and a runbook as files.

How soon can a data engineering project start?

Usually within one to two weeks of a signed scope, starting with the two-week diagnostic; the pilot then takes three to four weeks. Most waiting comes from source-system access, so we list every connection and named owner we need before kickoff.

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