Data Analytics Engineer | The Apprentice Project | ₹8-10 LPA

The Apprentice Project

The Apprentice Project works so that children from low-income communities in India can learn the 21st-century skills they need to thrive, like emotional intelligence, digital literacy and everyday money management. Millions of children grow up without these skills, which limits their chances and can keep families in poverty. The Apprentice Project reaches students where they already are, on WhatsApp, through a chatbot where they learn by doing hands-on projects. Every chat creates data about who signed up, who came back, what content reached them and what they did with it. This role sits within The Apprentice Project's Data team, which turns that data into clear answers that help the organisation decide which students to reach and whether its programs work.

Role

As the first hire on the Data team, the Data Analytics Engineer will own The Apprentice Project's data from end to end. This means the pipelines that bring data in, the models that make it usable, the systems that keep it clean, and the answers that help teams act. The role reports to the Associate Senior Manager - Data & MEAL (Monitoring, Evaluation, Accountability and Learning) and is fully remote within India. It suits a builder who likes to fix problems at the root, uses AI (artificial intelligence) tools to work faster while checking their output, and wants to grow into leading a data function.

Responsibilities

• Own and maintain ETL/ELT (extract, transform, load) pipelines into BigQuery for chatbot, registration and partner data, including scheduling, failures and backfills.
• Build, test and document dbt (data build tool) models that give teams one trusted view of each student across years, cohorts and programs.
• Clean and match data from many sources by removing duplicates, linking students across years and fixing phone numbers, names, schools and cohort IDs.
• Design systems that keep data clean from the start, such as checks when data comes in, standard formats, controlled form inputs and clear data entry guides for partner teams.
• Set up data quality tests and alerts for freshness, completeness and unusual patterns, and trace delivery gaps like students who did not receive their material back to the root cause.
• Answer questions from program and leadership teams on returning students, cohorts and retention, and build dashboards and regular reports that run automatically.
• Use GenAI (generative AI) tools every day to write and review code, create tests and documentation, and try AI-based approaches to hard problems like fuzzy name matching.
• Keep code in version control, reviewed and documented, and work with the Data & MEAL lead to set standards, shape the data roadmap and later mentor new hires.

Requirements

• 2 to 4 years of hands-on experience in data engineering, analytics engineering or a closely related role.
• Strong SQL (Structured Query Language) and solid dbt experience, including models, tests, documentation and incremental builds.
• Experience running production pipelines with scheduling, monitoring, error handling and backfills on BigQuery on GCP (Google Cloud Platform) or a similar cloud data warehouse.
• Experience cleaning messy real-world data from multiple sources, including deduplication and entity matching, and building checks to stop problems coming back.
• Comfort with Python for pipelines, cleaning and automation, and with Git and GitHub, including code review.
• Clear writing and the ability to explain what broke, what you did and what it means to someone non-technical.
• Good to have: experience with Glific, WhatsApp or other chatbot data, dashboard tools like Looker Studio or Metabase, basic statistics and A/B test analysis, open-source contributions, or work in education or the social sector.

Benefits

• Pay of ₹8-10 LPA (lakh per annum).
• Real ownership of the organisation's data layer from day one.
• A clear growth path to Senior Analytics Engineer or Data Lead as the team grows.
• Fully remote work from anywhere in India.
• Close work with program, product and leadership teams.
• A values-driven culture with honest, direct feedback.

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