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Data engineers reviewing pipelines and warehouse diagrams in a Harare office
# services

Data engineering is the design and build of reliable pipelines, warehouses, and aggregations so data from many systems can be trusted, combined, and used for reporting and analytics.

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Overview

Scattered spreadsheets, ERP exports, and SaaS APIs do not make a data platform. Easygrab designs and implements ETL pipelines, data warehouses, and source aggregations so finance, operations, and leadership work from one reliable model. We build on Microsoft Fabric, Azure, Snowflake, and complementary tools — then hand over documented refresh schedules your team can run.

Key Benefits

  • ETL Pipelines That Run: Extract, transform, and load from ERP, CRM, files, and APIs on a schedule you can trust.
  • Warehouse Design & Implementation: Dimensional models, lakehouses, and marts designed for how your teams actually query data.
  • Aggregation Across Sources: Combine finance, sales, inventory, and operations into one consistent set of measures.
  • Platforms That Scale: Microsoft Fabric, Azure, Snowflake, and related services chosen to fit volume, budget, and skills.
# our process

How We Deliver

  1. 01

    Source Discovery

    Map ERP, CRM, files, APIs, and operational systems that need to land together.

  2. 02

    ETL Pipeline Design

    Extract, transform, and load paths, quality rules, and incremental refresh.

  3. 03

    Warehouse Design

    Model and implement the warehouse or lakehouse your reports will sit on.

  4. 04

    Source Aggregation

    Unify measures and dimensions so different systems tell the same story.

  5. 05

    Operate & Handover

    Monitoring, access, documentation, and training for your data owners.

# faq

Frequently Asked Questions

Data engineering builds the pipelines, warehouses, and aggregations that move data from many sources into a reliable platform. Analytics and BI then report on top of that foundation.

Yes. We design and implement ETL/ELT pipelines and warehouse or lakehouse models — including incremental loads, quality checks, and documented refresh schedules.

We implement on Microsoft Fabric, Azure (including Data Factory and Synapse), Snowflake, Databricks, dbt, Spark, SQL, and Python. The stack follows your volume, budget, and existing licences.

Yes. We combine ERP, CRM, spreadsheets, databases, and API feeds into consistent dimensions and measures so leadership is not reconciling conflicting numbers.

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