Data Analytics Engineer

In India, 100 million children from low-income communities grow up without access to the 21st-century skills they need to thrive, from emotional intelligence and digital literacy to everyday money management. This gap leads to a lifetime of limited opportunity, intergenerational poverty, and rising mental health concerns.

India's classrooms are stuck in the past. We're building what kids need for the future.

At The Apprentice Project (TAP), we reach students where they already are: on Whats App. Through our chatbot, students learn through hands‑on projects and build skills that last.

Every one of those interactions generates data: who registered, who returned, what content reached them, and what they did with it. Today that data comes from many sources and is often messy. To answer the questions that shape our programs, we need data that is clean, connected and trustworthy.

That's where you come in.

As our Analytics Engineer , you'll be the first hire on the Data team and will own TAP's data layer end to end: the pipelines that bring data in, the models that make it usable, the systems that keep it clean, and the answers that help our teams act.

This isn't just backend work. It's infrastructure that fuels impact.

A 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.

Location:

Remote (India)

Experience:

2–4 years of relevant experience

Reports to:

Associate Senior Manager - Data & MEAL

Type:
Full-Time

Tech Stack: SQL, dbt, Big Query (GCP), Python, Git/Git Hub, Glific (Whats App chatbot platform), GenAI coding assistants

• Own and maintain our ETL/ELT pipelines into Big Query, including chatbot (Glific/Whats App) 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.

• 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.

• 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.

• 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.
• Treat every repeated task as a candidate for automation: recurring pulls, reports, checks and fixes.
• Use GenAI tools as part of…