Olee

Data Engineering

Clean data, in the right place, as it happens.

We bring data out of the systems that hold it, clean it, keep it in sync as it changes, and shape it for the reports, dashboards and AI that need it.

Start a projectSee how we work
  • Debezium
  • Kafka
  • Change data capture
  • PostgreSQL
  • MySQL
  • BigQuery
  • Python
  • Scheduled pipelines
  • Dashboards
  • Text to SQL

Why it matters

AI and analytics are only as good as the data under them. Most of the work is getting that data clean, current and in one place. That's the work we're good at.

What we build

Every step between raw data and a good decision.

  • Live sync

    Changes in one database show up in another within seconds, using change data capture instead of nightly dumps.

  • Pipelines

    Data pulled from every source, cleaned, joined and loaded on a schedule you can see and trust.

  • Cleaning and matching

    Duplicates merged, gaps filled and customers matched across systems, so one person is one record.

  • Analytics and dashboards

    The numbers your team asks for every week, always up to date and always the same answer.

  • Ask your data

    Questions in plain words turned into safe, read-only queries on approved tables, checked for accuracy.

  • Governed access

    Who can see what, written down and enforced, with sensitive fields kept out of reach.

How we work

One team, from first conversation to launch.

  1. 01

    Survey

    Every source, what's in it, how often it changes and who relies on it.

  2. 02

    Model

    One clear shape for the data, agreed with the people who'll use it.

  3. 03

    Build

    Pipelines and sync, tested against real volumes before they go live.

  4. 04

    Prove

    Numbers checked against the old reports until they match, or we know why.

  5. 05

    Run

    Monitoring, fixes and new features after launch, from the team that built it.

Built with tools we know inside out.

Sync
Debezium, Kafka, Change data capture
Store
PostgreSQL, MySQL, BigQuery
Process
Python, Scheduled pipelines
Use
Dashboards, Text to SQL, AI audiences

Case study

From scattered customer data to campaigns and analytics

Marketers describe an audience in plain words, run campaigns across WhatsApp, email, SMS and push, and see what worked.

Read the case study

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Let's get your data working.

Tell us where your data lives and what you wish it could tell you. We'll show you how to get there.

Talk to our engineersSee our work