Building data pipelines
Reliable ETL/ELT flows that move data from source systems into staging and warehouse layers.
Junior Data Engineer · Midrand, South Africa
Mpho Matebesi — turning raw data into something worth trusting.
I build and automate ETL pipelines with SQL Server, SSIS and Python, and I document everything as I go. Six months of hands-on training took me from SQL foundations to a complete end-to-end pipeline — and I'm now looking for a junior data engineering role.
- 6 months
- Structured data engineering training, Feb – Jul 2026
- 1 capstone
- End-to-end raw-to-analytics ETL pipeline, built and documented
- Core stack
- SQL Server · SSIS · Python · Azure · Git
What I bring to a team
Data quality & reliability
Validation, logging, error handling and row-level auditing so downstream numbers can be trusted.
Performance & scale
Query tuning, indexed loads and package optimisation as volumes grow past the comfortable size.
Modeling for analytics
Normalised sources modelled into clean, well-structured tables analysts can query without help.
Automation
Scheduled, monitored jobs with alerting and recovery instead of manual, weekend-breaking reruns.
Collaboration
Version-controlled work, documented repos and clear communication with analysts and stakeholders.
Alongside the technical work: attention to detail, clear written communication, curiosity, and the habit of documenting things so the next person doesn't have to guess.
Tech stack
Languages
- SQL (T-SQL)
- Python
ETL & Orchestration
- SSIS
- SQL Server Agent
- SSISDB
Databases
- Microsoft SQL Server
- MySQL Workbench
- Snowflake
- Databricks
Cloud
- Microsoft Azure
- BigQuery
Tooling
- Visual Studio
- VS Code
- GitHub
- Postman
Reporting
- Power BI
- Looker Studio
- Excel
February — July 2026
The six-month journey
The BrightLearn Data Engineering programme, month by month — what I learned and what I built with it.
- 01
February
Tools Setup & SQL Foundations
Set up a full local data stack — SQL Server, SSMS, SSIS, Python and VS Code — then built fluency in SELECT, WHERE, ORDER BY, aggregations with GROUP BY, and all four join types.
- SQL Server
- SSMS
- Joins
- Aggregations
- 02
March
Intermediate SQL & Automation
Moved into subqueries, CTEs and window functions, wrapped repeatable logic into stored procedures and user-defined functions, and scheduled the first automated jobs with SQL Server Agent.
- CTEs
- Window Functions
- Stored Procedures
- SQL Agent
- 03
April
SSIS Foundations
Learned ETL from the ground up in SSIS: control flow versus data flow, connection managers for databases and flat files, transformations, and cleansing and validation rules that keep bad rows out of the warehouse.
- SSIS
- ETL
- Connectors
- Data Quality
- 04
May
Advanced SSIS
Hardened packages with error handling, logging and row redirection, tuned performance on larger loads, deployed to SSISDB, and orchestrated the whole workflow on a schedule with SQL Server Agent.
- Error Handling
- Tuning
- SSISDB
- Orchestration
- 05
June
Python & GitHub
Wrote Python for extraction and transformation, handled files and messy data programmatically, connected Python directly to SQL Server, and adopted a professional Git workflow — branches, merges and pull requests.
- Python
- pandas
- Python–SQL
- Git / GitHub
- 06
July
Capstone — End-to-End Pipeline
Designed the data model, built a complete raw-to-analytics-ready ETL pipeline, tested and optimised it, then documented and presented the solution as a portfolio-grade project.
- Data Modeling
- Pipeline Build
- Testing
- Documentation
Open to junior data engineering roles.
If you're building pipelines and need someone who will learn your stack quickly and document as they go, I'd like to talk.