Data Engineer

Solve Education!; Sumur Bandung; On-site; IDR 539,471,318 - 899,118,863; Full time

Job summary

Solve Education! in Bandung, Indonesia is looking for a Data Engineer to build and manage the data infrastructure for our mission of accessible AI-powered learning. This role offers high ownership and autonomy.

You will design scalable data pipelines, ensure data quality, and partner with teams to translate business requirements into data solutions. Candidates should have 2–4 years in data engineering, strong SQL and Python skills, and experience with BigQuery and Apache Airflow. Join us in making a real impact!

Qualifications

• 2–4 years of experience in data engineering, database development, or related technical roles.
• Strong command of SQL and Python, with solid software-engineering habits.
• Deep, hands-on experience with BigQuery is required.

Responsibilities

• Design, build, and maintain scalable data pipelines in Apache Airflow.
• Own the data architecture for reliable, consistent data management.
• Model raw data into clean datasets for analysis.

Skills

SQL

Python

BigQuery

Apache Airflow

dbt/Dataform

Looker Studio

Metabase

MongoDB

GCP

AI tools

Job description

About the Role

Solve Education! is hiring a Data Engineer to build and own the data infrastructure behind our mission: helping young people everywhere reach their potential through accessible, AI-powered learning. This is a high-ownership role for an engineer who thrives on autonomy, sets a high bar for their own work, and wants their engineering to translate directly into real-world impact.

Bandung, Indonesia

Employment Type: Full-Time

Responsibilities

• Design, build, and maintain scalable pipelines in Apache Airflow to ingest data from multiple sources — including MongoDB — into BigQuery.
• Own the data architecture across ingestion, storage, and transformation layers, keeping it reliable, consistent, and high-quality.
• Model raw data into clean, structured, ready-to-consume datasets using dbt/Dataform, including turning event-level data into sessions and other analytical entities.
• Build and curate data sources and data marts that serve analytical and reporting needs across teams.
• Monitor pipeline health, troubleshoot failures, and ensure timely, accurate data delivery with minimal downtime.
• Optimize BigQuery query performance and manage storage/compute cost to keep the platform efficient.
• Implement automated testing, validation, and error handling to keep pipelines robust.
• Apply best practices in data modeling, security, and compliance, and safeguard data integrity across the platform.
• Implement governance, access controls, and documentation (metric definitions, lineage, business glossary) for secure, consistent data usage.
• Build dashboards and visualizations in Looker Studio and Metabase to support data-driven decisions.
• Partner with stakeholders to define metrics and enable reliable self-service analytics.
• Use AI-powered tools (e.g., Claude, GitHub Copilot, cloud-native AI services) to boost efficiency, scalability, and documentation quality.
• Continuously explore and adopt new technologies that streamline workflows and accelerate delivery.
• Work closely with product, engineering, and business teams to define data requirements, metrics, and self-service analytics needs.
• Translate business requirements into efficient, scalable technical solutions, enabling advanced analytics and ML initiatives.
• Maintain technical documentation and standards, track key performance metrics, and propose evidence-based improvements to the platform.

What We're Looking For

• 2–4 years of experience in data engineering, database development, or related technical roles.
• Strong command of SQL and Python, with solid software-engineering habits.
• Deep, hands-on experience with BigQuery is required.
• Hands-on experience with workflow orchestration in Apache Airflow.
• Experience modeling and transforming data with dbt or Dataform.
• Experience building dashboards in Looker Studio and/or Metabase.
• Working knowledge of GCP and experience with both relational and non-relational databases (e.g., MongoDB).
• Tech-savvy, with a habit of learning and applying AI tools in daily work.
• Ability to manage multiple priorities independently and under pressure, with high attention to detail, personal accountability, and strong problem-solving skills.

Bonus Points If You Have

• Real-time data streaming and event-driven architectures (e.g., Pub/Sub, Kafka).
• Experience standing up CI/CD pipelines for data engineering.
• Exposure to MLOps or machine learning pipeline deployment.
• A demonstrated track record of using AI tools (Claude, Copilot, LLM-based assistants, automation frameworks) to accelerate engineering work.
• Experience in mission-driven or non-profit organizations.