Senior Analytics Engineer

Job not on LinkedIn

🔥 12 hours ago

🦁 Africa – Remote

⏰ Full Time

🟠 Senior

📊 Analytics Engineer

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Imagine Worldwide

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2017

📚 Education

🤝 Non-profit

🌍 Social Impact

Education • Non-profit • Social Impact

Imagine Worldwide is a non-profit organization dedicated to empowering children globally through child-directed, tech-enabled learning. They provide high-quality education focused on literacy and numeracy, partnering with governments, organizations, and communities to ensure accessibility and affordability of learning resources. Their innovative approach aims to unlock each child's potential, improving health, wealth, and social outcomes in underserved regions across Africa and beyond.

📋 Description

• Build and maintain AWS infrastructure for the analytics platform, including storage, compute, networking, data catalogue, secrets and access management • Manage infrastructure as code in Terraform • Administer the ClickHouse Cloud warehouse, including configuration, users, roles and performance/capacity monitoring • Host and maintain self-hosted Apache Superset and Apache Airflow tools • Contribute to analytics platform architecture decisions • Build and maintain ingestion pipelines from application databases, APIs and operational systems using AWS Glue, Airbyte and AWS Lambda • Implement efficient and recoverable incremental loading patterns • Develop and optimise Spark jobs processing billions of device events • Model programme context including schools, terms, device allocations and enrolment • Design and maintain Apache Iceberg tables for performance and cost • Validate data at ingestion and investigate quality issues • Build and maintain Airflow workflows with dependencies, retries and alerting • Develop dbt staging and core models in ClickHouse • Implement incremental models balancing freshness, correctness and cost • Maintain dbt tests and model contracts • Document core models, lineage and approximations • Collaborate with the Senior BI & Data Analyst and Data Scientist • Build CI/CD pipelines in GitHub Actions • Implement monitoring and alerting across pipelines, warehouse and hosted tools • Respond to incidents, perform root cause analysis and support data quality processes • Maintain access controls and security practices • Monitor AWS and ClickHouse costs and optimise workloads • Maintain and test backup and recovery procedures • Participate in code reviews and write runbooks and technical documentation

🎯 Requirements

• Bachelor’s degree in Computer Science, Software Engineering or a related field, or equivalent practical experience; demonstrated experience is weighted equally to formal credentials • At least 7 years in data engineering, software engineering or platform engineering roles • At least 3 years building and running distributed data processing (Spark or equivalent) in production • Track record of responsibility for a production data platform or major parts of one, including reliability, performance and day-to-day operation • Experience with datasets in the hundreds of millions to billions of records • Experience making trade-offs between compute, storage and performance within real budget constraints • Experience managing cloud infrastructure for data workloads • Experience working in small teams with limited supervision • Experience working with remote teams across multiple countries and time zones • Strong Python with testing and modular design • Advanced SQL for analytical databases • Spark (PySpark), including performance tuning for partitioning, shuffles and data skew • Understanding of data lake design, Parquet, partitioning strategy, and lake-versus-warehouse processing • Hands-on AWS experience, including S3, Glue or EMR, IAM and EC2 • Infrastructure as code using Terraform, CloudFormation, CDK or similar • Linux administration and Docker • dbt in production, including incremental models and testing • Workflow orchestration with Airflow, Dagster, Prefect or similar • Git, CI/CD and code review practice • Strongly preferred: Apache Iceberg or equivalent, ClickHouse or comparable warehouse, Terraform, Apache Airflow, AWS Glue and Lake Formation, and analytics data modelling • Advantageous: Databricks, Kafka/Flink/Kinesis/Spark Structured Streaming, event or IoT data, Airbyte, Apache Superset administration, and API or systems integration development

🏖️ Benefits

• Full-time remote position • Occasional international travel for in-person team meetings, field support or training, as needed • Equal opportunity employment