{"id":3157686,"url":"https://alion.io/job/imagine-worldwide-senior-analytics-engineer","title":"Senior Analytics Engineer","company":{"id":679437,"name":"Imagine Worldwide","domain":"imagineworldwide.com","url":"https://alion.io/company/imagineworldwide","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-29T05:49:05Z"}},"role":"Analytics","role_family":"Analytics","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":[],"countries":[],"hiring_countries":["EG","KE","NG","ZA","DZ","AO","BJ","BW","BF","BI","CM","CV","TD","CF","KM","CD","DJ","GQ","ER","SZ","ET","GA","GM","GH","GN","GW","CI","LS","LR","MG","MW","ML","MR","MU","YT","MA","MZ","NA","NE","CG"],"hiring_countries_total":56,"salary":null,"salary_estimate":{"min_usd":80000,"max_usd":163000,"period":"year","method":"global_role_seniority_cell","sample_n":3468},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon EC2","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"ClickHouse","optional":false},{"name":"dbt","optional":false},{"name":"GitHub Actions","optional":false},{"name":"IAM","optional":false},{"name":"Spark","optional":false},{"name":"Superset","optional":false},{"name":"Terraform","optional":false},{"name":"Amazon Kinesis","optional":true},{"name":"Amazon Redshift","optional":true},{"name":"Apache Hudi","optional":true},{"name":"Apache Kafka","optional":true},{"name":"CloudFormation","optional":true},{"name":"Dagster","optional":true},{"name":"Databricks","optional":true},{"name":"Delta Lake","optional":true},{"name":"Docker","optional":true},{"name":"Flink","optional":true},{"name":"Git","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Linux","optional":true},{"name":"Platform Engineering","optional":true},{"name":"Prefect","optional":true},{"name":"pySpark","optional":true},{"name":"Python","optional":true},{"name":"Snowflake","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-10-23T11:04:36Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-29T09:27:42Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"About Imagine Worldwide\nAll children have immense potential, but hundreds of millions don’t have access to the learning they need. Imagine Worldwide believes that every child should be empowered with the basic right to literacy and numeracy. We provide an innovative education technology solution and implementation model to the global literacy/numeracy learning crisis using the onebillion application and various toolkits and systems to support implementation. Our model is massively scalable, evidence-based, and can deliver foundational learning to millions of the most marginalized children.\nImagine Worldwide partners with governments, organizations, and communities to provide child-directed, tablet-based learning that is accessible, effective, and affordable. We are incorporated in California (United States) as a nonprofit organization, but operate in Sub-Saharan Africa through locally-registered entities in Malawi, Sierra Leone, and Tanzania. Learn more on our website.\nRole Overview\nThe Senior Analytics Engineer builds, runs and improves Imagine’s analytics platform: the infrastructure, pipelines and core data models that the organisation’s reporting and analysis depend on. The central task is to scale the platform as data volumes grow, keeping costs under control without giving up performance. In a small team, that means covering the infrastructure underneath the data as well as the data engineering itself.\nImagine’s data comes from many sources. The largest is tablets in more than 1,500 schools across Malawi, Tanzania and Sierra Leone, which record learning activity as individual events, adding up to billions of records. These only become meaningful when joined with programme context, such as active schools per term, device allocations and enrolment, and with operational data from inventory and field data collection. Turning all of this into reliable data on a non-profit budget calls for careful decisions about where processing happens and what each workload costs.\nThe role reports to the Director of Analytics, who sets the platform’s architecture and technical standards, and works alongside the rest of the Analytics Team. We are looking for someone senior who takes initiative, turns architectural direction into working systems, and brings forward evidence-based proposals for improving them. As the engineer builds context, they take on increasing ownership of the platform’s day-to-day running and technical decisions.\nKey Responsibilities\nThe Senior Analytics Engineer’s responsibilities fall into four pillars. The first two cover the platform itself: the infrastructure it runs on, and the pipelines that bring data in from many sources and process it at scale. The third covers orchestration and the core data models that the rest of the Analytics Team builds on. The fourth covers the practices that keep the platform reliable, secure and affordable to run.\nData Platform and Infrastructure\nImagine’s analytics platform runs on AWS and ClickHouse Cloud, with infrastructure managed as code. In a small team, this role carries the infrastructure work that a larger organisation would split across dedicated platform or DevOps staff, limited to the scope of the analytics platform.\nBuild and maintain the platform’s AWS infrastructure, including storage, compute, networking, the data catalogue, secrets and access management (S3, EC2, Glue Data Catalog, Lake Formation, IAM, Secrets Manager).\nManage infrastructure as code in Terraform, keeping environments reproducible, version-controlled and reviewable.\nAdminister the ClickHouse Cloud warehouse, including service configuration, user and role management, and monitoring of performance and capacity.\nHost and maintain the team’s self-hosted tools, including Apache Superset for BI and Apache Airflow for orchestration. This covers deployment, upgrades, authentication and availability. The Senior BI & Data Analyst manages the dashboards and content within Superset.\nContribute to architectural decisions on how the platform evolves, setting out options and trade-offs for review with the Director of Analytics.\nLarge-Scale Data Processing and Pipelines\nData reaches the platform from application databases, operational systems and field data tools, and lands in an Apache Iceberg data lake on S3. Spark-based processing on AWS Glue does the heavy work before the data moves into ClickHouse for analysis. The aim is to do expensive processing once, as far upstream as practical, so that downstream models and queries stay fast and affordable.\nBuild and maintain ingestion pipelines from the platform’s sources, including application databases, APIs and operational systems, using tools such as AWS Glue, Airbyte and AWS Lambda.\nImplement incremental loading patterns that stay efficient and recoverable as volumes grow, landing raw data in the lake in a form that preserves its full history.\nDevelop and optimise Spark jobs that process billions of device events into learning sessions and higher-level aggregates, paying close attention to partitioning, skew and compute cost.\nBring in and model the programme context that gives event data its meaning, such as schools, terms, device allocations and enrolment, and handle how that context changes over time.\nDesign and maintain Iceberg tables for performance and cost, including partitioning strategy, file sizing, compaction and snapshot management.\nValidate data at ingestion and surface quality issues early, working with the Analytics Team to trace them back to their source.\nContribute to evaluating new processing approaches as volumes grow, including moving more work upstream and assessing where streaming would add value.\nOrchestration and Transformation\nApache Airflow orchestrates the platform’s pipelines, and dbt transforms data in ClickHouse into the models that reporting and analysis rely on. This role builds and maintains the foundational layers of those models.\nBuild and maintain Airflow workflows that coordinate ingestion, processing and transformation, with clear dependencies, retries and alerting.\nDevelop and maintain dbt staging and core models in ClickHouse, following consistent conventions for naming, structure and materialisation.\nImplement incremental models for high-volume tables, choosing materialisation strategies and table designs that balance freshness, correctness and cost.\nWrite and maintain dbt tests and model contracts for core datasets, so that changes that would break downstream reporting are caught before they reach production.\nDocument core models such as learning sessions as agreed definitions, including their lineage, and make clear where a model applies an approximation rather than a direct transformation.\nWork with the Senior BI & Data Analyst on the boundary between core and reporting models, and with the Data Scientist on datasets for research and evaluation.\nReliability, Cost and Engineering Practice\nA small team needs a platform that runs predictably and that more than one person understands. This pillar covers the engineering practices that make that possible.\nBuild and maintain CI/CD pipelines in GitHub Actions for infrastructure, pipeline and dbt code, including automated checks that stop unsafe changes from reaching production.\nImplement monitoring and alerting across pipelines, the warehouse and hosted tools, so that failures and delays are visible quickly and reach the right people.\nRespond to platform incidents and data issues, carry out root cause analysis, and contribute to the team’s data quality incident process.\nApply and maintain access controls and security practices across the data lake, warehouse and BI layer, in line with Imagine’s data governance policies.\nMonitor platform costs across AWS and ClickHouse, track the cost and performance of individual workloads over time, and use that evidence to identify savings and prioritise optimisation work.\nMaintain and periodically test backup and recovery procedures for the platform’s core components.\nTake part in code review and write clear runbooks and technical documentation, following the team’s documentation standards, so that knowledge of the platform is shared across the team.\nRequirements\nThe qualifications below are grouped by importance. Core requirements are needed to do the role from the start. Strongly preferred skills shorten the time it takes to become effective on Imagine’s platform. Advantageous experience adds value but is not expected. We do not expect any one candidate to bring everything listed here.\nEducation\nA bachelor’s degree in Computer Science, Software Engineering or a related field, or equivalent practical experience. Demonstrated experience is weighted equally to formal credentials.\nExperience\nAt least 7 years in data engineering, software engineering or platform engineering roles, including at least 3 years building and running distributed data processing (Spark or equivalent) in production.\nA track record of taking responsibility for a production data platform, or major parts of one, including its reliability, performance and day-to-day operation.\nExperience working with datasets in the hundreds of millions to billions of records, and designing processing that stays efficient as volumes grow.\nExperience running data workloads within real budget constraints: making deliberate trade-offs between compute, storage and performance, and being able to show the effect of those decisions.\nExperience managing the cloud infrastructure that data workloads run on, not only using managed services.\nExperience in small teams where engineers cover a broad scope and work with limited supervision.\nExperience working with remote teams across multiple countries and time zones.\nExperience in the non-profit, international development or social sector is an advantage but not a requirement.\nTechnical Skills: Core\nStrong Python, written to production standards, with testing and modular design.\nAdvanced SQL, including writing and optimising queries against analytical databases.\nSpark (PySpark), including performance tuning for partitioning, shuffles and data skew.\nA solid understanding of data lake design, including columnar formats such as Parquet, partitioning strategy, and when to process in the lake rather than the warehouse.\nHands-on AWS experience with data services and the infrastructure around them, such as S3, Glue or EMR, IAM and EC2.\nInfrastructure as code, using Terraform, CloudFormation, CDK or similar.\nLinux administration and Docker, to the level needed to deploy and maintain self-hosted services.\ndbt in production, including incremental models and testing.\nWorkflow orchestration with Airflow, Dagster, Prefect or similar.\nGit, CI/CD and code review practice.\nTechnical Skills: Strongly Preferred\nApache Iceberg, or another open table format such as Delta Lake or Hudi.\nClickHouse, or a comparable columnar warehouse such as Snowflake, BigQuery or Redshift.\nTerraform specifically.\nApache Airflow specifically.\nAWS Glue and Lake Formation.\nData modelling for analytics, including handling reference data that changes over time.\nTechnical Skills: Advantageous\nManaged Spark platforms such as Databricks.\nStreaming technologies such as Kafka, Flink, Kinesis or Spark Structured Streaming.\nEvent, telemetry or IoT data, particularly from devices that sync intermittently.\nIngestion tools such as Airbyte.\nAdministering BI tools such as Apache Superset.\nSoftware development beyond data pipelines, such as building APIs or system integrations.\nCompensation & Benefits\nSalary is competitive and commensurate with experience.\nLocation\nThe Senior Analytics Engineer must be based in Africa. The role will frequently collaborate and coordinate with operations teams across Africa, the US, and the UK. \nThis is a full-time remote position; the role can be based anywhere in Africa with reliable internet access and alignment with core working hours across time zones. 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