BI Data Analytics Engineer
2026-07-31T08:16:31+00:00
D.light
https://cdn.greatkenyanjobs.com/jsjobsdata/data/employer/comp_2965/logo/d.light.png
https://www.dlight.com/
FULL_TIME
Nairobi
Nairobi
00100
Kenya
Professional, Scientific, and Technical Services
Computer & IT, Science & Engineering, Business Operations
2026-08-07T17:00:00+00:00
8
Background information about the job or company (e.g., role context, company overview)
d.light is a global leader and pioneer in delivering affordable solar-powered solutions designed for the two billion people in the developing world without access to reliable energy. d.light provides distributed solar energy solutions for households and small businesses that are transforming the way people all over the world use and pay for energy. Through f...
Responsibilities or duties
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.
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.
Qualifications or requirements (e.g., education, skills)
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. We are looking for someone with genuine data engineering depth who is comfortable writing production-grade Python, not only SQL.
Experience needed
3+ years experience in analytics/data engineering.
Any other provided details (e.g., benefits, work environment, team info, or additional notes)
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.
* 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.
* Data warehousing concepts * Modern data warehouse architectures * ELT and orchestration tooling (Airbyte, Dagster, dbt or comparable) * SQL (performance tuning, modeling for analytical workloads) * Python (data pipelines, API integrations, transformation logic) * Cloud data warehouse (Redshift) * Data quality and testing frameworks (dbt tests, Great Expectations) * Pipeline observability, monitoring, and alerting * Git, CI/CD, environment management * AI-assisted tooling (LLM copilots) * API integrations (OAuth2, token-based authentication, pagination, rate limiting, incremental/event-driven ingestion)
* Solid grounding in data warehousing concepts and modern data warehouse architectures. * 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.
JOB-6a6c59dfa7bc9
Vacancy title:
BI Data Analytics Engineer
[Type: FULL_TIME, Industry: Professional, Scientific, and Technical Services, Category: Computer & IT, Science & Engineering, Business Operations]
Jobs at:
D.light
Deadline of this Job:
Friday, August 7 2026
Duty Station:
Nairobi | Nairobi
Summary
Date Posted: Friday, July 31 2026, Base Salary: Not Disclosed
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JOB DETAILS:
Background information about the job or company (e.g., role context, company overview)
d.light is a global leader and pioneer in delivering affordable solar-powered solutions designed for the two billion people in the developing world without access to reliable energy. d.light provides distributed solar energy solutions for households and small businesses that are transforming the way people all over the world use and pay for energy. Through f...
Responsibilities or duties
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.
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.
Qualifications or requirements (e.g., education, skills)
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. We are looking for someone with genuine data engineering depth who is comfortable writing production-grade Python, not only SQL.
Experience needed
3+ years experience in analytics/data engineering.
Any other provided details (e.g., benefits, work environment, team info, or additional notes)
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.
Work Hours: 8
Experience in Months: 12
Level of Education: bachelor degree
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