Data Analytics Engineer

What you will do

Data Pipelines & Modelling

• Own and maintain our ETL/ELT pipelines into BigQuery, including chatbot (Glific/WhatsApp) data, registration data and partner records.
• Build, test and document dbt models that give teams a single, trusted view of students across years, cohorts and programs.
• Handle scheduling, orchestration, failures and backfills, so pipelines run reliably without manual babysitting.

Data Cleaning & Clean-Data Systems

• Clean, standardise and reconcile data from multiple sources: deduplicate records, match students across years and sources, and fix inconsistent phone numbers, names, schools and cohort identifiers.
• Go beyond cleaning: build systems that produce clean data by default, such as validation at ingestion, standard formats, controlled inputs for forms and sheets, and clear data entry guidelines for the teams that send us data.
• Maintain lineage and documentation so everyone knows where a number came from and how far to trust it.

Monitoring & Reliability

• Set up data quality tests and monitoring for freshness, completeness and anomalies, so problems are caught by alerts, not by users.
• Investigate delivery gaps, such as students who didn't receive messages or material, by tracing the problem through bot flows, data and pipelines, then fixing the root cause.

Answering Questions That Drive Decisions

• Answer data questions from program and leadership teams, such as returning-student counts, cohort and retention analysis, and content delivery gaps, quickly and with clear explanations.
• Build and maintain the dashboards and recurring reports teams rely on, and automate them wherever possible.

Automation & AI-Driven Efficiency

• Treat every repeated task as a candidate for automation: recurring pulls, reports, checks and fixes.
• Use GenAI tools as part of your daily workflow to write and review SQL and dbt code, generate tests and documentation, and debug faster, while verifying everything they produce.
• Explore AI-assisted approaches to hard data problems, such as fuzzy matching of student and school names, or classifying free-text responses.

Building the Function

• Keep code version-controlled, reviewed and documented, so TAP's data never depends on one person.
• Work with the Data & MEAL lead to shape the data roadmap, set standards, and eventually onboard and mentor the next hires.

Who We're Looking For

An builder who hates doing the same thing twice. You see a messy spreadsheet and think about how to stop it from ever arriving messy again. You're as comfortable tracing why 2,000 students didn't receive their course material as you are writing a dbt model, and you use AI tools to get there faster without giving up rigour.If you want to own a data function from the ground up and grow into leading it, we want to meet you.

• You have 2–4 years of hands-on experience in data engineering, analytics engineering or a closely related role.
• You write strong SQL and have solid dbt experience: models, tests, documentation and incremental builds.
• You've run production pipelines, including scheduling, monitoring, error handling and backfills, on BigQuery or a comparable cloud warehouse.
• You've cleaned messy, real-world data from multiple sources, including deduplication and entity matching, and you've built checks to stop it recurring.
• You use Python for pipelines, cleaning and automation, and you're fluent with Git/GitHub, including code review.
• You use GenAI tools to work faster, and you know where they fail and how to check their output.
• You write clearly: you can explain what broke, what you did, and what it means to someone non-technical.
• You take ownership: you notice problems before others do and see work through to the end.
• You're excited about applying data engineering to education, equity and social impact.