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Solution Engineer at Digital Divide Data (DDD Kenya)
Digital Divide Data (DDD Kenya)
September 14, 2026
Full-time
On-site
Job Description
Design and build robust data ingestion pipelines, transforming raw CSV/TSV data into structured, queryable datasets.
Model custom entities and map diverse data sources into standardised target schemas.
Build and manage data environments using SQLite, Google Cloud SQL and BigQuery.
Develop and maintain Dockerised services for data loading, embedding generation and data-serving applications.
Deploy and operate production workloads on Google Cloud Run, with a focus on scalability, reliability and high availability.
Work across Redis, VPC networking, Artifact Registry and Cloud Storage.
Implement secure cloud architectures using Cloud IAM, VPC and Cloud Run ingress controls.
Automate data workflows and deployments through GitHub Actions, including scheduled ingestion, ID mapping, embedding generation and container builds.
Build and integrate AI-powered capabilities, including natural-language querying, semantic search, embeddings and LLM-powered workflows.
Contribute to emerging agentic architectures and integrations, including technologies such as Model Context Protocol (MCP).
Qualifications
2+ years' experience in data engineering, cloud engineering or backend development.
Strong Python development skills, including practical experience with Pandas.
Solid experience with Docker and containerised applications.
Strong working knowledge of Google Cloud Platform, particularly cloud compute, managed databases, data warehousing, networking and IAM.
Strong SQL skills and experience working with both relational and analytical data stores.
Experience designing, building and consuming REST APIs.
A strong engineering mindset, with the ability to work independently, troubleshoot complex problems and take ownership from development through to production.
It's a plus if you have
Experience with AI/ML engineering, particularly embeddings, Sentence Transformers or LLM applications.
Exposure to agentic frameworks, MCP or other emerging AI integration patterns.
Experience with Flask and Jinja.
Familiarity with WSL and Google Cloud Shell.
Design and build robust data ingestion pipelines, transforming raw CSV/TSV data into structured, queryable datasets.
Model custom entities and map diverse data sources into standardised target schemas.
Build and manage data environments using SQLite, Google Cloud SQL and BigQuery.
Develop and maintain Dockerised services for data loading, embedding generation and data-serving applications.
Deploy and operate production workloads on Google Cloud Run, with a focus on scalability, reliability and high availability.
Work across Redis, VPC networking, Artifact Registry and Cloud Storage.
Implement secure cloud architectures using Cloud IAM, VPC and Cloud Run ingress controls.
Automate data workflows and deployments through GitHub Actions, including scheduled ingestion, ID mapping, embedding generation and container builds.
Build and integrate AI-powered capabilities, including natural-language querying, semantic search, embeddings and LLM-powered workflows.
Contribute to emerging agentic architectures and integrations, including technologies such as Model Context Protocol (MCP).
Qualifications
2+ years' experience in data engineering, cloud engineering or backend development.
Strong Python development skills, including practical experience with Pandas.
Solid experience with Docker and containerised applications.
Strong working knowledge of Google Cloud Platform, particularly cloud compute, managed databases, data warehousing, networking and IAM.
Strong SQL skills and experience working with both relational and analytical data stores.
Experience designing, building and consuming REST APIs.
A strong engineering mindset, with the ability to work independently, troubleshoot complex problems and take ownership from development through to production.
It's a plus if you have
Experience with AI/ML engineering, particularly embeddings, Sentence Transformers or LLM applications.
Exposure to agentic frameworks, MCP or other emerging AI integration patterns.
Experience with Flask and Jinja.
Familiarity with WSL and Google Cloud Shell.