- International development
- Data engineering
- ETL pipelines
Building ETL Pipelines and Trusted Datasets for a United Nations Agency
How BEEU TECH helped a United Nations agency turn data from multiple backend systems into clean, standardized datasets through automated ETL pipelines, reusable dataflows and built-in quality checks.
Our actual experience delivering great tech and talent.
Engagement overview
- Industry
- International development
- Reach
- Programs across regions
- Engagement model
- Embedded data engineer
- Approach
- ETL and ELT pipelines, curated datasets
- Engagement status
- Delivered, with ongoing support
1. The client
Our client is a United Nations agency that runs programs across regions and reports their outcomes to internal and external stakeholders.
Its teams rely on accurate, timely and well-structured data to make evidence-based decisions and monitor how programs perform.
That data lived in several backend applications, in different formats and structures, and much of it was sensitive program information.
2. The challenge
Fragmented data and slow, manual reporting
Program data sat across multiple backend applications and systems, often in different formats and structures. Clean, standardized datasets for reporting were hard to come by, and reports were slow to reach the people who needed them.
Five challenges stood between the data and the people who needed it.
Fragmented sources
Data was spread across multiple backend applications, in different formats and structures.
Few clean datasets
Clean, standardized datasets for reporting and analytics were in short supply.
Manual extraction
Manual extraction and transformation raised the risk of errors and delays.
Slow access to reports
End users struggled to get timely, reliable reports for program monitoring and decisions.
Governance and sensitivity
Sensitive program data had to stay consistent, accurate and governed at every step.
The agency needed more than new reports. It needed a dependable path from raw backend data to datasets its teams could use with confidence.
3. The solution
A centralized, automated data engineering pipeline
BEEU TECH's data engineer designed a centralized, automated pipeline that extracts data from the backend applications, transforms it into structured datasets and builds reusable dataflows.
Reporting and analytics tools read these standardized datasets directly, so every report starts from the same, checked data.
Six pillars of the data pipeline
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Source analysis
Analyzed the backend data sources and business requirements with the agency's program and reporting teams.
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ETL and ELT pipelines
Built pipelines to extract, clean, transform and load data into analytical datasets.
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Curated datasets and dataflows
Created optimized dataflows and curated datasets for consistency and reuse.
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Validation and quality checks
Built data validation and quality checks into the pipeline to maintain accuracy and integrity.
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Governance and privacy
Kept every step within the agency's data governance, security and privacy standards.
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Reporting support
Documented the datasets and supported the reports and dashboards that end users rely on.
4. The engineering team
BEEU TECH's data engineer worked with the agency's program and reporting teams, from the first requirements through to release and support.
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Data Engineer
Designed the pipelines, built the datasets and dataflows, set up quality checks and supported the reporting teams.
Working directly with the people who use the reports kept every dataset tied to a real reporting need.
5. The implementation journey
A phased approach, from requirements to reporting
The project moved from understanding the data to supporting the reports built on it.
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Phase 1
Requirement gathering and analysis
The first phase focused on understanding the data sources, the reporting needs and the challenges behind them.
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Phase 2
Pipeline and dataset development
BEEU TECH designed and built the extraction, the transformations and the dataflows that produce the datasets.
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Phase 3
Testing and validation
Data quality checks, validation with stakeholders and performance tuning made sure the datasets were right before release.
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Phase 4
Deployment and reporting support
The datasets were released for reporting, with ongoing support for enhancements and maintenance.
6. The business impact
Better data, faster decisions
Standardized, checked datasets changed how the agency reports, from the time it takes to the trust people place in the numbers.
Before and after the data pipeline
| Before | After |
|---|---|
| Data scattered across backend systems | Centralized, standardized datasets |
| Manual extraction and transformation | Automated pipelines and reusable dataflows |
| Errors and delays from manual handling | Less manual handling, fewer errors |
| Hard to get timely, reliable reports | Timely, reliable reports for decisions |
| Limited trust in the data | More trust in data among stakeholders and program teams |
| New sources slow to add | New sources and reports added efficiently |
7. Long-term value
A data foundation that grows with the programs
The pipeline turned fragmented backend data into datasets the agency can rely on, and it was built to take on more.
New data sources and reporting requirements can be added efficiently, and every dataset carries the same validation and governance.
The engagement shows how focused data engineering can improve reporting efficiency, data quality and decision-making in an organization whose programs depend on data.
8. Data architecture
- Backend applicationsProgram data sources
- ETL and ELT pipelinesExtract, clean, transform, load
- Curated datasetsReusable dataflows
- Reports and dashboardsUsed by program teams
9. Engineer perspective
“This project strengthened [the agency’s] data infrastructure by transforming fragmented backend data into reliable, actionable insights.”
BEEU TECH · TECHNOLOGY DELIVERY
Data engineering your reports can stand on.
Still building reports by hand from scattered systems? BEEU TECH's data engineers build the pipelines, datasets and checks that make reporting timely and reliable.
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