Data Engineer
AI summary
Raising The Village is hiring a Data Engineer to join its expanding data infrastructure team in Mbarara, Uganda. The role focuses on building batch and streaming pipelines, maintaining Delta Lake warehouse layers, and supporting ML and AI platforms that power field analytics and program evaluation. The position reports to the Senior Data Scientist within the VENN department and closes on 22 Sep.
- Core builder role on RTV’s expanding data infrastructure team
- Based in Mbarara, Uganda, with field-data and analytics focus
- Involves Databricks, PySpark, Delta Lake, and ML pipeline support
- Application deadline is 22 Sep
AI job guide
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AI salary guide
Not enough public dataNot enough public salary data is available for this exact role. Before applying, prepare to ask about gross pay, benefits, contract length, probation period, transport and any allowances.
Can you qualify for this role?
- UnclearRelated work experienceThe text mentions experience, but the exact level should be confirmed at source.
- RequiredEducation or certification mentioned in the postThe captured text mentions education, a diploma, certificate, or licence.
- PreferredPractical evidence in factory, internship, no_experienceThe tags and summary point to skills connected with this role.
- RequiredAvailability to work in Not specifiedThe vacancy is associated with this location.
Documents to prepare
- Likely requiredUpdated CV
- Role specificCover letter or short employer message
- OptionalProfessional references
- Role specificAcademic or professional certificates
- VerifyID or passport only after verifying the employer
Application tips for this job
- Place your strongest Data Engineer evidence in the first half of your CV.
- In your cover letter or employer message, connect your experience to Raising The Village and the role in Not specified.
- Add concrete examples related to factory, internship, no_experience, ideally with measurable outcomes or clear responsibilities.
- Follow the instructions from Ugandan Jobline; avoid sending documents to unofficial contacts or copied links.
- Confirm the deadline, interview location and employer contact before sharing personal documents.
- Prepare a polite question about pay, benefits and contract terms for later interview stages.
Source and safety check
- Ugandan Jobline
- Original source link available
- Application method is clear
- Deadline not specified
- No major risk signal was detected in the captured text.
Never pay for interviews, shortlisting, medical checks, uniforms, or job placement. Confirm every application at the original source before sharing personal documents. Report suspicious listing.
Interview preparation
- What experience makes you a strong fit for this Data Engineer role in factory, internship?
- How have you handled responsibilities similar to those in this job post?
- Are you available to work in Not specified under the listed contract or schedule?
- Prepare examples with clear responsibilities, tools used and measurable outcomes.
- Review the source and research Raising The Village before the interview.
Ask what the first priorities will be in the role and how success will be measured.
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Original source description
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Data Engineer Jobs – Raising The Village
Raising The Village
Posted
9 hours ago
Closes 22 Sep
☆
Job Title:
Data Engineer
Organisation:
Raising The Village
Duty Station:
Mbarara, Uganda
About Organisation:
Raising The Village International (RTV) is a Canadian non-profit organization focused on ending extreme poverty by eliminating immediate barriers of scarcity, nurturing income-generation activities and building local capacity, while moving communities toward economic self-sufficiency. Raising The Village is a fast-growing organization on an accelerated growth path. Our East Africa and North American teams work together to lift communities out of ultra-poverty in last-mile villages. We operate at the intersection of direct implementation and advanced data analytics to inform progress, decision-making, and impact.
Job
Summary
The Data Engineer is a core builder on RTV’s expanding data infrastructure team, reporting directly to the Senior Data Scientist within the VENN department, and responsible for designing, developing, and maintaining the pipelines, warehouse layers, and data quality systems that power RTV’s programmatic analytics, machine learning platforms, and field evaluation tools. Operating at the intersection of data platform engineering, ML infrastructure support, and field data integration, this role works across a fast-moving roadmap that spans batch and streaming ingestion, ELT pipeline development, Delta Lake architecture, observability frameworks, and the integration of structured field data with AI model outputs.
Key Duties and
Responsibilities
- Pipeline Development & Delivery
- Design and deliver batch and streaming data pipelines that ingest data from field collection platforms (SurveyCTO, ArcGIS, custom mobile apps) into RTV’s Databricks warehouse, working at pace against an accelerated infrastructure roadmap.
- Implement ELT and ETL workflows in PySpark and Databricks SQL, applying Medallion Architecture (Bronze, Silver, Gold) principles to produce clean, versioned, and consumption-ready data layers.
- Contribute actively to sprint-based delivery cycles, taking ownership of pipeline workstreams end-to-end from design through deployment and monitoring.
- Delta Lake & Warehouse Architecture
- Build and maintain Delta Lake table structures with strong schema enforcement, ACID-compliant write patterns, and time travel capabilities to support auditability across program datasets.
- Contribute to data modeling decisions including; star schema design, SCD patterns, and denormalization trade-offs.
- Support the evolution of Unity Catalog governance structures including lineage tracking, access controls, and dataset documentation as the warehouse scales across new program domains and geographies.
- Data Observability & Quality
- Implement and maintain data observability frameworks across all pipeline stages, covering data freshness, volume anomalies, schema drift detection, and SLA monitoring.
- Build validation and quality checks at ingestion and transformation layers using tools such as Great Expectations, dbt tests, or Databricks-native monitoring capabilities.
- Establish structured logging, alerting, and incident response practices that give the team fast, reliable visibility into pipeline health across all environments.
- ML & AI Pipeline Support
- Integrate structured household data, image classification outputs, and ML model predictions into unified warehouse layers for consumption by Data Scientists and the WorkMate AI platform.
- Collaborate with ML Engineers and Data Scientists to build and maintain feature engineering pipelines and training data preparation workflows that support RTV’s computer vision and adoption scoring systems.
- Collaboration & Documentation
- Work closely with the Senior Data Engineer on roadmap prioritization, architectural decisions, and engineering standards as the team scales.
- Partner with Software Engineers, Data Scientists, field evaluation teams, and program staff to understand data
Requirements
- and translate them into reliable, well-documented pipeline solutions.
- Maintain thorough documentation of pipeline architectures, transformation logic, data dictionaries, and runbooks to support team growth and organizational knowledge continuity.
- Qualifications, Skills and
Experience
- A Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, Information Systems, Statistics, or a related quantitative field is preferred.
- Equivalent practical
- through demonstrable project work, open-source contributions, or bootcamp training is equally welcome.
- Clear evidence of building and shipping production-grade pipelines, including demonstrable ownership from design through deployment with specific examples of integrating, moving, and transforming data at a meaningful scale.
- Candidates must demonstrate proficiency in Python (including Pandas), PySpark, and advanced SQL for building and transforming data at scale within a Databricks-first environment spanning Delta Lake, Unity Catalog, and Medallion Architecture.
- Core engineering competencies include ELT/ETL design, batch and Spark Structured Streaming pipelines, and data observability practices covering validation, monitoring, and alerting frameworks.
- Supporting skills include Git-based collaborative workflows, working knowledge of AWS core services, and the ability to produce clean, visualization-ready datasets for consumption in tools such as Power BI, Tableau, or Python visualization libraries.
- How to Apply:
- All suitably qualified and Interested applicants should apply online at the link below.
- Opens the employer’s application page
- Apply Now →
- NB: Only shortlisted candidates will be contacted.
- For more of the latest jobs, please visit
- https://www.theugandanjobline.com
- or find us on our facebook page
- https://www.facebook.com/UgandanJobline
- Level of
- in Months: no
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Education
- bachelor degree
- Work Hours: 8