Innocent Nhamo

Data Engineer with a BSc in Mathematical Statistics, DP-700 Microsoft Fabric Data Engineer certification, and hands-on experience delivering data engineering, analytics, and automation solutions across consulting and financial services environments. Experienced in designing and optimizing ETL/ELT pipelines, data reconciliation frameworks, large-scale data processing, SQL development, workflow automation, cloud-based data solutions, and business intelligence reporting. Proven ability to work with high-volume datasets exceeding 45 million records using SQL Server, Python, DuckDB, Polars, Microsoft Fabric, Power BI, and cloud technologies.

Strong foundation in data modeling, data warehousing, analytics engineering, statistical analysis, data quality management, and performance optimization. Passionate about transforming raw data into trusted, scalable, business-ready data products that improve operational efficiency and support decision-making.

Data Consultant (Contract) at QJ Data Solutions (2026-01 – Present)

• Design and maintain data processing and reconciliation pipelines supporting client reporting and operational decision-making.
• Process and validate datasets exceeding 45 million records using Python, SQL Server, DuckDB, and Polars.
• Improved large-scale import and transformation performance by implementing optimized data processing workflows.
• Develop automated reconciliation frameworks that reduce manual effort, improve accuracy, and accelerate reporting delivery.
• Build repeatable ETL processes for importing, validating, transforming, and reporting data across multiple source systems.
• Create exception reporting and data quality controls to identify anomalies and reconciliation breaks.
• Develop automated web data extraction solutions using Playwright to support downstream reporting pipelines.
• Support financial and operational reporting processes involving transactions and reporting batches exceeding ZAR 75 million.
• Collaborate with stakeholders to translate business requirements into scalable data solutions and reporting outputs.

Data Science Apprentice — AML Compliance Learning Programme (Apprenticeship) at ABSA Group (2024-07)

• Completed apprenticeship learning focused on financial data analysis, AML compliance concepts, fraud detection, and transaction monitoring.
• Gained exposure to how ETL pipelines, anomaly detection, and compliance reporting support financial crime analysis.
• Practiced interpreting Power BI dashboards and analytics outputs used for fraud detection, transaction monitoring, and compliance review.
• Learned how customer profile data, behavioural patterns, and statistical thinking can be used to identify higher-risk entities.
• Gained exposure to Python, SAS Analytics, machine learning concepts, and model evaluation in a financial services learning environment.

Cloud Practitioner Training at AWS re/Start Program (2025-09 – 2025-11)

• Completed hands-on AWS cloud training covering EC2, S3, Lambda, RDS, monitoring, troubleshooting, and cloud foundations.
• Built cloud workflows using Python and shell scripting, strengthening automation and pipeline support skills.

BSc in Mathematical Statistics – University of Pretoria