We are seeking a Data Analytics Engineer to join our dynamic team. This role involves building and maintaining our data infrastructure, including working with tools like Redshift, dbt, and Dagster, laying the foundation for all the work of our Business Intelligence team and the single source of truth reports we build and maintain to serve the wider business.
The ideal candidate will have a solid foundation in data warehousing concepts, ETL processes, and a strong proficiency in SQL. If you are passionate about data engineering and eager to contribute to a project that significantly impacts our business, we invite you to apply.
Beyond building and maintaining our data infrastructure, this role owns the ingestion layer end-to-end not only orchestrating pre-built connectors, but designing and maintaining custom API integrations for source systems that lack off-the-shelf support. We are looking for someone with genuine data engineering depth who is comfortable writing production-grade Python, not only SQL.
Roles & Responsibilities
- Develop and maintain robust data pipelines from multiple source systems using Airbyte, Redshift, dbt, dlt and Dagster.
- Implement data transformations and ensure data quality and integrity.
- Collaborate with cross-functional teams to meet data requirements.
- Contribute to improving and scaling our data infrastructure.
- Provide support in data modeling and ETL processes.
- Collaborate with other members of the BI team to ensure data quality and consistency across reports and visualizations.
- Design, build, and maintain custom API integrations to ingest data from source systems that lack pre-built connectors (e.g. payment and mobile-money platforms, PayGo systems, CRM, and internal applications), handling authentication, pagination, rate limiting, and retry logic.
- Instrument pipelines for observability, monitoring, alerting, and automated data-quality testing so freshness and accuracy issues are caught before they reach stakeholders.
- Manage and maintain code through Git-based workflows and CI/CD.
Key Performance Indicators
- Sprint Velocity: We work using an agile setup and it's important that we close the tickets we are assigned in a sprint.
- Data Availability: Is the data refreshed on-time each day for us to use it?
- Data Warehouse Performance: Uptime, query speed, ETL speed.
- Data Quality: We need to deliver reports that accurately showcase business performance.
- Documentation: To be lean and maximize our impact we need strong systems. Everyone is expected to contribute by documenting their work.
- Stakeholder Satisfaction: As a team we'll be measured on how well the business thinks we've done (internal NPS).
Requirements
- Solid grounding in data warehousing concepts and modern data warehouse architectures.
- 3+ years experience in analytics/data engineering.
- Experience with modern ELT and orchestration tooling such as Airbyte, Dagster & dbt or comparable tools for building data pipelines.
- Strong SQL skills including performance tuning and modelling for analytical workloads.
- Strong Python skills for building data pipelines, API integrations, and transformation logic.
- Working experience with a cloud data warehouse, ideally Redshift, including awareness of query performance and cost.
- Experience with data-quality and testing frameworks (e.g. dbt tests, Great Expectations) and pipeline observability, monitoring, and alerting.
- Familiarity with Git, CI/CD, and environment management (dev / staging / production) for analytics code.
- Comfortable using AI-assisted tooling (LLM copilots) to accelerate development and documentation.
- Hands-on experience building and maintaining API integrations including OAuth2 or token-based authentication, pagination, rate limiting, and incremental or event-driven ingestion.
