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.
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Built in your own cloud, warehouse and repositories · Confidentiality and data processing agreements signed before any data moves · Overlap hours agreed at kickoff
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.
Pipelines, warehouses and modelled tables that feed reporting and AI, with schemas, scheduling, monitoring and documented lineage your team can read.
Deduplication, standardisation, validation rules and a quality scorecard, so the same customer is one record in every system.
Metric definitions, cohort and funnel analysis, and reporting built on modelled tables rather than on exported spreadsheets.
Dashboards built around the decision they support, with defined metrics, sensible defaults and no chart that nobody uses.
Forecasting, scoring, segmentation and recommendation models trained on your own data, with measured accuracy and a retraining plan.
Named engineers working continuously on your data backlog, inside your own cloud, repositories and ticketing, priced per engineer per month.
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.
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.
| 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 |
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.
| 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 |
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.
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.
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.
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.
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
| 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 |
Pricing comes first; ownership and timing follow.
More answersEvery 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.
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.
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.
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.
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.
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