Every organisation now depends on timely, accurate data, yet the path from operational systems to a management report is often a tangle of manual exports and unreliable spreadsheets. Data engineering replaces that tangle with repeatable pipelines, well-designed databases and clear definitions, while analytics skills ensure the output answers real business questions.

This ten-day programme combines database foundations, pipeline engineering and enterprise application data. Participants start with relational modelling and SQL at an advanced level, then learn how to design schemas, tune queries and manage indexes, transactions and backups. They then build ETL and ELT pipelines with Python, handle files, APIs and database sources, validate data quality, schedule workflows and log failures. Dimensional modelling follows, with facts, dimensions, slowly changing attributes and the design of a departmental warehouse.

A dedicated stream examines ERP data: how transactional tables are organised, how to extract data safely without disturbing production, how to reconcile figures with the general ledger, and how to define metrics for finance, supply chain and workforce reporting. Later sessions address cloud platforms, security, governance, documentation and performance monitoring. Learning is lab-based throughout, and participants finish with an integrated capstone project that moves sample ERP data through a pipeline into a warehouse and a dashboard.

By the end of the course, participants will be able to:

• Design normalised relational schemas and write advanced SQL including window functions.
• Tune queries and manage indexes, transactions and backups.
• Build ETL and ELT pipelines in Python and pandas from files, APIs and databases.
• Automate and monitor workflows using orchestration tools.
• Model dimensional warehouses with fact tables and slowly changing dimensions.
• Extract and interpret ERP transactional data for finance, procurement and inventory reporting.
• Implement data quality tests and reconciliations against source totals.
• Deliver analytical dashboards that rely on engineered data.

The course is suited to technical and analytical professionals working with organisational data, including:

• Data engineers and ETL developers
• Database administrators and architects
• Business intelligence and data analysts
• ERP functional consultants and super users
• Finance and accounting systems staff
• IT auditors and risk analysts
• Software developers moving into data work
• Statisticians and M&E data managers

Participants leave with:

• A library of advanced SQL patterns and tuning notes.
• A working Python ETL pipeline with data quality tests.
• A star schema design for a business area of their choice.
• A map of key ERP tables and extraction rules.
• A reconciliation routine linking warehouse and ledger totals.
• A capstone project and documentation they can show to employers or managers.

The course is laboratory-driven, with roughly two-thirds of the time spent at the keyboard, through:

• Short concept sessions followed by guided SQL and Python labs
• Shared practice databases and anonymised ERP extracts
• Code review and troubleshooting clinics
• Team build of a warehouse and dashboard
• A capstone presented to the class for critique

Participants who attend all ten days and complete the labs and capstone are awarded a Certificate of Completion by Vision Reach Global Consultancy, accredited for continuing professional development (CPD).