Background

DDD believes talent has no boundaries--and opportunities shouldn’t either. In 2001, we saw the need to bring tech skills and living-wage work to men and women in underserved communities in Asia. It was here that DDD helped plant the seed for a socially responsible outsourcing practice known as impact sourcing.

Responsibilities

• 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.

Additional Information

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).

• Python
• Pandas
• Docker
• Google Cloud Platform
• SQL
• REST APIs
• AI/ML engineering
• Embeddings
• Sentence Transformers
• LLM applications
• Flask
• Jinja

• 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.
• 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.