Data Engineering and Analytics

One trustworthy source of truth, and reporting people believe.

Algoramming builds data engineering and analytics from Dhaka, Bangladesh for teams across the UAE, Qatar, Saudi Arabia, the US, the UK, and Australia. We bring your scattered data into one reliable pipeline and warehouse, then build the dashboards and reporting on top, so the whole team makes decisions from numbers everyone actually trusts.

We bring your scattered data into one reliable pipeline and warehouse, then build the dashboards on top, so decisions come from numbers the whole team trusts rather than three conflicting spreadsheets.

Discipline
Cloud, DevOps & data
Cadence
Two-week shipping rhythm
Team
Senior engineers & designers
Source code
Yours from commit one
A deeper look

Everything to know about data and analytics.

What this specialism means in practice, written for the people who buy it and the people who will live inside the product after launch.

01

The problem is rarely too little data

Most teams do not lack data; they lack one version of it they can trust. The numbers live in different tools, defined differently, and every meeting starts by arguing about whose spreadsheet is right. We fix that at the root: reliable pipelines pull the sources together, a modelled warehouse defines each metric once, and dashboards read from that single layer. The argument about whose number is correct simply goes away, which is worth more than any single chart.

02

One source of truth instead of conflicting spreadsheets

A dashboard is only as trusted as the pipeline behind it, and trust collapses the first time a number is visibly wrong. We build data quality checks into the pipelines, monitor them, and document how each metric is defined and where it comes from. When someone asks why a figure looks off, there is a traceable answer rather than a shrug. That reliability is what turns analytics from a thing people doubt into a thing they act on.

What you get

Outcomes, not deliverables.

We measure success in shipped value, not tickets closed. Every engagement is anchored to a few outcomes both sides can defend.

  1. 01

    One source of truth instead of conflicting spreadsheets

  2. 02

    Reporting the whole team actually trusts

  3. 03

    Data pipelines that run reliably and can be traced

  4. 04

    Faster answers to the questions leadership keeps asking

What we deliver

Concrete artifacts, not slide decks.

Everything lands in your repositories, your cloud, and your control. Nothing is locked behind us.

  1. Artifact

    Data pipelines from your sources into a warehouse

  2. Artifact

    A modelled, documented warehouse layer

  3. Artifact

    Dashboards and reporting for the key metrics

  4. Artifact

    Data quality checks and pipeline monitoring

Our process

The same senior team, the same playbook.

Data and analytics runs on the cloud, devops & data playbook. Boring on purpose, predictable by design.

  1. 01Discover
  2. 02Design
  3. 03Build
  4. 04Launch
  5. 05Support
  1. 1

    Discover

    We assess the current infrastructure, delivery process, and data sources, and agree the cost and reliability targets.

    Phase 01 / 05
  2. 2

    Design

    We design the infrastructure-as-code, the pipeline, and the data model before changing anything live.

    Phase 02 / 05
  3. 3

    Build

    We build environments, automate the pipeline, and stand up data pipelines with quality checks in your accounts.

    Phase 03 / 05
  4. 4

    Launch

    We cut over in reversible stages, verify on both sides, and keep a rollback path throughout.

    Phase 04 / 05
  5. 5

    Support

    We monitor cost, reliability, and data quality, and keep the foundation healthy as the product grows.

    Phase 05 / 05
Our toolkit

Pragmatic tools. Senior judgement.

The everyday kit our team reaches for on this work. None of it is sacred; every choice is justified against the problem.

  • 01AWS
  • 02GCP
  • 03Cloudflare
  • 04Terraform
  • 05Docker
  • 06GitHub Actions
  • 07PostgreSQL
  • 08dbt
  • 09Grafana
  • 10Datadog
Common questions

Things teams ask before signing.

Have a different one? Send a single email; we usually answer within a business day.

Which warehouse and BI tools do you use?
We choose based on your scale and team, common warehouses paired with a BI tool your people can use, and set them up in your accounts so the data stays yours.
Can you connect all our different data sources?
Typically yes. We build pipelines from your databases, SaaS tools, and files into one warehouse, with quality checks so what lands is trustworthy.
Do you build the pipeline, the warehouse, and the dashboards?
All three, if you want. The pipelines pull your sources together, the modelled warehouse defines each metric once, and the dashboards read from that single layer, so the value is visible to the people who need it and every number traces back to a definition.
How do you make sure a number in a dashboard is correct?
We build data quality checks into the pipelines, monitor them, and document how each metric is defined and where it comes from. When a figure looks off, there is a traceable answer rather than a shrug, which is what lets people trust the dashboard enough to act on it.
Part of cloud, devops & data

Explore the rest of cloud, devops & data.

Cloud, DevOps and Data

Infrastructure that scales quietly and a data layer you can trust.

Discipline overview
Specialism

Cloud Infrastructure and Hosting

Infrastructure that scales quietly and stays in your name.

See specialism
Specialism

DevOps and CI/CD

Ship often, safely, with a pipeline you can trust.

See specialism
Specialism

Cloud Migration

Move to the cloud without moving your problems with you.

See specialism
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