CloudFactory is changing the way the world works by providing an on-demand, digital workforce for scaling critical business processes in the cloud. We’re also on a mission to create meaningful work for as many people as possible.

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Senior Data Engineer

Job Type

Full Time

Qualification

BA/BSc/HND

Experience

3 - 5 years

Location

Nairobi

Job Field

Data, Business Analysis and AI , ICT / Computer

Role Summary

As a Senior Data Engineer, you will own and evolve CloudFactory’s data platforms, pipelines, and analytical foundations, transforming complex, multi-source data into reliable, business-ready datasets. You will work business-first, partnering closely with product, operations, and leadership teams to define metrics, design scalable data models, and ensure high standards of data quality, governance, and performance. By driving modern data engineering best practices, mentoring teammates, and proactively improving our data ecosystem, you will enable confident, data-driven decision-making across the organization.

Responsibilities

• Design, build, and maintain scalable data pipelines and analytics architecture across cloud and on-prem environments
• Develop reliable analytical data models that transform raw data into business-ready datasets.
• Define, implement, and own key business metrics, including semantic layers and supporting data structures.
• Ensure high data quality and reliability by identifying inconsistencies, resolving data issues, and improving monitoring and governance practices.
• Partner with product, operations, finance, and GTM teams to understand analytics needs and deliver actionable insights.
• Manage and optimize data pipeline orchestration, including third-party data ingestions and cost/performance improvements.
• Lead end-to-end ownership of major reporting domains, ensuring accuracy, health, and long-term scalability.
• Contribute to data engineering standards, documentation, and best practices.
• Mentor junior team members and support a culture of continuous improvement.
• Participate in incident response, on-call rotations, and post-mortems, proactively improving system reliability.

Requirements

• 3–5+ years of experience in Analytics Engineering or Data Engineering.
• Expert-level SQL, including complex queries and performance optimization.
• Strong Python skills.
• Hands-on experience with dbt for data transformation and modeling.
• Solid understanding of analytical data modeling techniques.
• Experience working with both batch and streaming data.
• Experience with Snowflake, ClickHouse, or similar data warehouse technologies.
• Hands-on experience with ETL/ELT tools such as Fivetran, Airbyte, PeerDB, or similar.
• Experience integrating data from APIs and multiple data sources.
• Experience with data orchestration tools, preferably Prefect.
• Experience with BI and reporting tools such as Looker, QuickSight, Grafana, or similar.
• Familiarity with AWS services (e.g., S3, ECS/Fargate, SNS/SQS).
• Experience with version control and CI/CD practices in analytics workflows (e.g., Git/GitHub, GitHub Actions).
• Strong problem-solving skills and attention to detail.
• Ability to work independently and manage multiple priorities in a fast-paced environment.
• Strong communication and collaboration skills, with the ability to explain technical concepts to non-technical stakeholders.
• Customer-focused mindset and comfort working closely with business teams.

Nice-to-have

• Familiarity with data governance, lineage, and documentation tools (e.g., DataHub, Great Expectations).
• Experience with IaC (Terraform, CloudFormation, etc.)

Benefits

• Great Mission and Culture
• Meaningful Work
• Market competitive salary
• Quarterly variable compensation
• Remote and Home working
• Comprehensive medical cover
• Group life insurance
• Personal development and growth opportunities
• Office snacks and lunch
• Periodic team building and social events

• Design, build, and maintain scalable data pipelines and analytics architecture across cloud and on-prem environments
• Develop reliable analytical data models that transform raw data into business-ready datasets.
• Define, implement, and own key business metrics, including semantic layers and supporting data structures.
• Ensure high data quality and reliability by identifying inconsistencies, resolving data issues, and improving monitoring and governance practices.
• Partner with product, operations, finance, and GTM teams to understand analytics needs and deliver actionable insights.
• Manage and optimize data pipeline orchestration, including third-party data ingestions and cost/performance improvements.
• Lead end-to-end ownership of major reporting domains, ensuring accuracy, health, and long-term scalability.
• Contribute to data engineering standards, documentation, and best practices.
• Mentor junior team members and support a culture of continuous improvement.
• Participate in incident response, on-call rotations, and post-mortems, proactively improving system reliability.

• Expert-level SQL, including complex queries and performance optimization.
• Strong Python skills.
• Hands-on experience with dbt for data transformation and modeling.
• Solid understanding of analytical data modeling techniques.
• Experience working with both batch and streaming data.
• Experience with Snowflake, ClickHouse, or similar data warehouse technologies.
• Hands-on experience with ETL/ELT tools such as Fivetran, Airbyte, PeerDB, or similar.
• Experience integrating data from APIs and multiple data sources.
• Experience with data orchestration tools, preferably Prefect.
• Experience with BI and reporting tools such as Looker, QuickSight, Grafana, or similar.
• Familiarity with AWS services (e.g., S3, ECS/Fargate, SNS/SQS).
• Experience with version control and CI/CD practices in analytics workflows (e.g., Git/GitHub, GitHub Actions).
• Strong problem-solving skills and attention to detail.
• Ability to work independently and manage multiple priorities in a fast-paced environment.
• Strong communication and collaboration skills, with the ability to explain technical concepts to non-technical stakeholders.
• Customer-focused mindset and comfort working closely with business teams.
• Familiarity with data governance, lineage, and documentation tools (e.g., DataHub, Great Expectations).
• Experience with IaC (Terraform, CloudFormation, etc.)

• BA/BSc/HND
• 3–5+ years of experience in Analytics Engineering or Data Engineering.