Data Engineering: AI-Ready Data
Data engineering services connect the systems where your records already live — CRM, ERP, billing, HR and product databases — and deliver one clean, governed copy of them on a schedule: a cloud data warehouse your dashboards, forecasts and AI models can trust. As a Dhaka-based company, we build it inside your own cloud, for international companies, at a fixed price per step, with a QA lead who reconciles every loaded number with your finance figures before you see a dashboard.
One exit at each step. No long-term commitment at any of them. We start with the two or three source systems behind the numbers you argue about most. Every source is mapped, the platform you already pay for is chosen over a new one, and every route to one reconciled warehouse is costed in your own numbers.
You get a written verdict — a warehouse and pipelines, a dashboard first, or nothing yet. If nothing in it justifies a build, you stop here.
30 minutes 01 Scope · free call
2 weeks 02 Diagnose · $1,500, half credited to the pilot
3–4 weeks 03 Pilot · $4,000 fixed
4–8 weeks 04 Production · from $6,000, quoted after the pilot
Monthly 05 Managed Ops · from $750 a month, optional
What is included in data engineering services?
Data engineering services include six deliverables: a source audit with a pipeline map, ETL/ELT pipelines that load every source on a schedule, a cloud data warehouse on the platform you already run, data quality tests and governance rules, AI-ready datasets documented for models and retrieval, and orchestration with alerts and a runbook. You receive a running platform, not a diagram.
Source audit and pipeline map
Every system that holds a number you report on, its owner, its keys and its refresh rate, mapped before any pipeline is written.
ETL/ELT pipeline development
Pipelines that extract from CRM, ERP, billing and product databases, transform in SQL and load on a schedule, with every step logged.
Cloud data warehouse or lakehouse
One governed store on Snowflake, Databricks, BigQuery or Microsoft Fabric, modelled so finance, sales and operations read the same figures.
Data quality and governance
Tests on every load for duplicates, nulls and broken keys; a catalogue of tables and owners; personal fields tagged and access controlled.
AI-ready datasets
Clean, joined, documented tables and document collections that a model, a retrieval system or an agent can use without manual clean-up.
Orchestration, monitoring and runbook
Scheduled runs on Airflow or your platform’s scheduler, alerts when a load fails or drifts, and a runbook your own team can follow.
Not included: Cloud and warehouse platform costs, bought in your name · a new platform licence when the one you already pay for will do · work outside the agreed sources, priced first as a change.
Finance
Revenue, cash and cost tables reconciled with the ledger, loaded from ERP, billing and bank exports.
Sales and marketing
Pipeline, churn and campaign tables joined on one customer key, from CRM, billing and ad platforms.
Operations
Orders, stock, deliveries and tickets in one warehouse, refreshed hourly where the shop floor needs it.
HR and payroll
Headcount, attrition and payroll tables from HRIS and payroll, with personal fields tagged and access controlled.
What does an AI-ready data architecture look like in data engineering?
An AI-ready data architecture in data engineering has four layers: source systems such as CRM, ERP and billing; scheduled ETL/ELT pipelines that log every step; a cloud data warehouse where data is loaded raw, tested and modelled on one customer key; and documented datasets that dashboards, machine learning models and AI agents all read from.
Sources
CRM
ERP
Billing
HRIS
Product database
Pipelines
ETL/ELT on a schedulenightly load at 02:00, every step logged
Transform in SQLdbt models, Airflow or your platform's scheduler
Alertsa failed or drifting load pages the owner
Cloud data warehouse
Rawloaded as received · 14 tables
Tested22 checks for duplicates, nulls and broken keys
Modelledone customer key across sales and billing
Reconciled · a person signsrevenue matches the ledger to 0.1%
AI-ready datasets feed
Dashboardsone set of figures for finance and sales
Forecasts and ML modelsclean history on stable keys
AI agents and retrievaldocumented tables and document collections
Alerts when a load fails or drifts, a catalogue of tables and owners, and a runbook your team runs.
Illustrative example. Yellow marks the step where a person signs; every load is logged with source, row counts and test results.
How do data engineering services work, from source audit to Managed Ops?
Data engineering services with us run in five steps: a free scoping call, a two-week audit that maps your sources and picks the platform, a three-to-four-week pilot that connects the sources and loads one reconciled warehouse, a four-to-eight-week production build with all sources, orchestration and alerts, then Managed Ops. Every step ends with a written result you can stop on.
01 30 min · free
Scope A call about the numbers you cannot trust and the systems they come from. If the sources are known, you leave with a quote.
02 2 weeks · credited
Diagnose Every source, owner, key and refresh rate mapped; your existing platform chosen; then a written verdict — build, a dashboard first, or not yet.
03 3–4 weeks · fixed price
Pilot Two or three sources connected, one warehouse loaded on a schedule, tests written, and the loaded figures reconciled with finance in a go/no-go note.
04 4–8 weeks · quoted after pilot
Production All agreed sources, orchestration, alerts, a catalogue of tables and owners, team training on the runbook and 30 days of fixes after handover.
05 Monthly · optional
Managed Ops Failed-load response, schema-change fixes when a source changes, one new source or table a month and a monthly review call. Cancel any month.
How long does a first data warehouse take?
A first working warehouse takes five to six weeks: a two-week audit, then a three-to-four-week pilot that loads one reconciled warehouse. Production takes four to eight weeks more.
Runbook Alerts, failed-load steps and table owners your own team can follow
Platform Snowflake, Databricks, BigQuery or Microsoft Fabric — yours
Tools Python, SQL, dbt, Airflow and your platform’s scheduler
Sources CRM, ERP, billing, HRIS, product databases, spreadsheets
QA Tests on every load, reconciliation with finance, logged runs
Hosting Your cloud, your warehouse account, your repositories
Do you need data engineering, a dashboard, or a machine learning model?
Not every data problem needs data engineering. One source system that already holds clean records needs a dashboard on top of it; records that already sit in one trusted place and a question about the future need a machine learning pilot; several sources moved by exports and copy-paste need pipelines first. Five questions show which fits.
1. How many systems hold the numbers you report on?
2. Is there one place where the cleaned data lives today?
3. How does data move between systems today?
4. What will the data feed first?
5. How fresh must it be?
A warehouse and pipelines, piloted first
Several sources, no single clean copy, moved by hand or by scripts: a pilot connects the sources and loads one reconciled warehouse in three to four weeks.
Sample pipeline run log · sales and billing warehouse
1
Sources connectedCRM, billing and ERP · 3 systems, 14 tables
2
Pipelines builtNightly load at 02:00 · every step logged
3
Tests written22 checks for duplicates, nulls and broken keys
4
ReconciliationMonthly revenue matches the ledger to 0.1%
5
Handed overWarehouse live · alerts set · runbook signed
Illustrative run
Yellow = a person signs
Every load logged
How the verdict is decided: one source and one clean place → a dashboard first · data already in one place and a prediction wanted → a machine learning pilot · no single clean copy, or moved by exports → a warehouse and pipelines, piloted first · pipelines exist on scripts → hardening and Managed Ops.
Why choose us as your data engineering company?
A data engineering company is judged on whether the numbers in the warehouse match the numbers finance signs, not on the diagram. We reconcile every pilot with your ledger, price each step in writing, keep personal fields tagged and masked under a processor agreement that follows the GDPR, and deliver data engineering solutions on the platform you already run.
Without a reconciled build
!!!!!
- A warehouse whose revenue figure nobody in finance will sign
- A new platform sold to you when yours would do
- Hourly quotes that grow as each source turns out messier than promised
- Pipelines that only the engineer who left knows how to run
With EICRA
Pilot report · Sales warehouse
Sources3Tables14Tests22/22Reconciled99.9%VerdictGo to production
Illustrative example
- Every pilot reconciled with your ledger before production is quoted
- Built on the platform you already pay for, with open tools
- Fixed prices at every step; production quoted in writing after the pilot
- Tests, alerts and a runbook your own team runs from day one
Is it safe to outsource data engineering to an offshore team?
Outsourcing data engineering to an offshore team is safe when access, transfer and ownership are settled in writing before any record moves. As a Bangladesh-based company, we build inside your cloud and your warehouse account with least-privilege access, tag personal fields before they are loaded, sign a data processing agreement first, and lose our access at handover. Reviewed By Eicra.com team
Which data engineering agreements are signed, and when?
NDA — mutual, signed before any credential, schema or export is shared with anyone.
DPA — processor terms under Article 28(3) of the GDPR and the equivalent national law, for any personal data.
International data transfers — standard contractual clauses or the transfer instrument your jurisdiction requires, signed before personal data moves.
Access — a least-privilege service role in your cloud and warehouse; credentials never leave your accounts.
Certifications — listed only when held; none are claimed on this page or in any proposal.
What data engineering controls, IP and rework terms apply?
Your cloud Pipelines run and data lands in your cloud and warehouse accounts; nothing is copied to our side.
Personal fields Names, emails and IDs tagged in the catalogue and masked in every table that does not need them.
Logged loads Every load logged with source, row counts, test results and time, so any figure can be traced.
Ownership Pipelines, warehouse schema, tests and runbook are yours under the contract; our access is removed at handover.
Rework Free when a delivered pipeline fails its agreed tests or reconciliation; new sources priced first as a change.
What proof do you get before you pay for data engineering?
Before you pay for data engineering, you get evidence instead of promises: measured commitments, a pilot warehouse reconciled with your own ledger before production is quoted, and a free 30-minute source review. Client case studies with numbers are added here as clients give permission to name them.
3–4 weeks
To a first warehouse loaded from your sources and reconciled with your finance figures.
Every load
Tested for duplicates, nulls and broken keys, and logged with source, row counts and time.
30 days
Of fixes after handover, with orchestration, alerts and a runbook your own team runs.
Case studies: client results with numbers are added here as clients give permission to name them. Ask on the scoping call for references in your industry.
What do buyers ask a data engineering company?
How much do data engineering services cost?
Our data engineering services are fixed-price per step, and each price is on the price cards at the top: a two-week data audit half credited to the pilot, a three-to-four-week pilot that connects your sources and loads one reconciled warehouse, a production build quoted in writing after the pilot, and optional Managed Ops.
What is data engineering as a service?
Data engineering as a service means an outside team builds and runs your data platform: it connects the systems where your records live, builds pipelines that load them on a schedule, keeps one governed warehouse, and hands over the tests, alerts and runbook. With us, Managed Ops then keeps the pipelines running month by month.
Will ETL be replaced by AI?
Not the pipeline, only some of the typing. AI tools now write much of the transformation code and documentation, which is why our pilots are short. What they cannot do is decide which of three revenue figures is right, reconcile it with finance, or take responsibility when a load fails overnight. A person still owns the pipeline.
What is AI-ready data?
AI-ready data is data a model, a retrieval system or an agent can use without manual clean-up: complete, documented, fresh on a known schedule, joined on stable keys, and governed so personal fields are known and protected. The warehouse is where readiness is built, and every warehouse we build ships with documented, tested datasets.
Who owns the pipelines, the warehouse and our data?
You do. Pipelines, warehouse schema, tests, orchestration and runbook are built in your cloud and repositories and stay yours; ownership is written into the contract. Your data never leaves your accounts; our offshore team works through a least-privilege role, signs a data processing agreement before personal data is touched, and is removed at handover.
Start with a free 30-minute source review or the two-week audit.