Services

Data Engineering Services in South Africa

Reliable pipelines and data warehouses that bring your data together for reporting and AI.

Discuss your project

Data engineering is building the infrastructure that collects, cleans, stores and moves data so it can be trusted for reporting, analytics and AI. It sits underneath dashboards: if the pipelines are unreliable, the numbers on the dashboard will be too.

Mohteknologies designs and builds ETL/ELT pipelines, data warehouses and data models that bring together information from your operational systems, spreadsheets and third-party platforms.

Problems we solve

  • Reports compiled by hand from several exports every week or month
  • Different departments quoting different numbers for the same metric
  • Data spread across systems with no single source of truth
  • AI or analytics initiatives held back by messy, inaccessible data

Who it's for

  • Businesses whose reporting depends on manual spreadsheet work
  • Companies preparing data for analytics or AI
  • Teams with existing pipelines that are slow or unreliable

Why work with us on data engineering

One source of truth

Consistent, documented definitions for key business metrics.

Automated and scheduled

Data refreshed automatically, with alerts when something fails.

Ready for analytics and AI

Clean, modelled data that BI tools and AI features can use directly.

How we deliver

Every engagement follows our development process, adapted to the work involved.

  1. 01

    Data audit

    We list your data sources, how data is used today, and where quality problems come from.

  2. 02

    Architecture

    Warehouse, pipeline tooling and refresh schedule chosen to fit your volumes and budget.

  3. 03

    Pipeline build

    Ingestion, transformation and data quality tests built incrementally, source by source.

  4. 04

    Documentation and handover

    Data dictionary, lineage and runbooks so your team understands the system.

  5. 05

    Launch and support

    We deploy to production, set up monitoring and hand over documentation. After launch we stay on for bug fixes, improvements and new features under a support arrangement that suits you.

Technologies

  • Python
  • SQL
  • PostgreSQL
  • Cloud data warehouses
  • dbt
  • Docker
  • AWS
  • Google Cloud

Integrations

  • Operational databases
  • Accounting, CRM and ERP systems
  • Spreadsheets and file drops
  • Third-party APIs
  • BI tools such as Power BI

Security considerations

  • Access controls on sensitive datasets and masking of personal information
  • POPIA-aligned retention and minimisation of personal data
  • Encrypted storage and audited access

Performance and reliability

  • Incremental loading to keep refresh times short
  • Partitioning and modelling for fast queries as data grows

Data Engineering: frequently asked questions

What is the difference between data engineering and data analytics?

Data engineering builds the pipelines and storage that make data reliable. Data analytics uses that data to answer business questions through reports, dashboards and analysis.

Do we need a data warehouse?

If you report across several systems or have more data than spreadsheets can handle comfortably, a warehouse usually saves time and errors. For small data volumes a simpler setup can be enough.

Can you work with our existing reporting tools?

Yes. We can feed tools such as Power BI from a well-modelled data source.

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Further reading

Let's talk about what you're building

Whether you have a detailed spec or a rough idea, we'd like to hear about it. Book a free 30-minute discovery call and get an honest view of the right approach.

Contact our software development team