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.

You’ll design, build, and maintain the data systems that power our global programs — from data pipelines to analytics-ready warehouses — covering full-cycle engineering, performance optimization, and governance. You’ll operate with a high degree of independence, make sound decisions without waiting for direction, and ship reliable solutions in a fast-moving environment. This is a role for someone who leads by doing.

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Before You Apply

We want to be upfront about how we work. This role moves quickly and trusts you with real ownership. You’ll handle ambiguity, juggle multiple priorities, and deliver production-ready work — sometimes as requirements shift mid-week or feedback needs a fast turnaround. We look for people who stay composed under pressure, take full accountability for their work, and hold themselves to a high standard without close supervision.

If you’re looking for a role that will stretch your technical range, sharpen your problem-solving, and connect your work to a mission that matters, we’d love to hear from you.

Responsibilities

Data Infrastructure & Pipelines

• Design, build, and maintain scalable data pipelines using Apache Airflow to ingest data from multiple sources — including the application database (MongoDB) — into the data warehouse (BigQuery).
• Maintain the data architecture across the ingestion, storage, and transformation layers to ensure quality, consistency, and reliability.
• Model raw data into clean, structured, ready-to-consume datasets, including transforming 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.

Performance and Reliability

• Monitor pipeline health, troubleshoot failures, and ensure timely, accurate data delivery with minimal downtime.
• Optimize query performance and storage/compute cost across the data platform to maintain efficiency.
• Implement automated testing, validation, and error handling to keep pipelines robust.

Data Quality & Governance

• Apply best practices in data modeling, security, and compliance, and ensure data integrity across all platforms.
• Implement data governance, access controls, and documentation (metric definitions, lineage, business glossary) to ensure secure and consistent data usage.

Analytics & BI Enablement

• Develop dashboards and visualizations in Looker Studio and other BI tools to support data-driven decision making.
• Partner with stakeholders to define metrics and enable reliable self-service analytics.

AI-Enhanced Workflows

• Use AI-powered tools (e.g., Claude, ChatGPT, GitHub Copilot, cloud-native AI services) to boost efficiency, scalability, and documentation quality.
• Continuously explore and adopt new technologies to streamline workflows and accelerate delivery.

Collaboration

• 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 machine learning initiatives.

Documentation & Reporting

• Prepare and maintain technical documentation, standards, and recommendations for the evolution of the data platform.
• Track key performance metrics and propose evidence-based improvements.

What We’re Looking For

• 1–3 years of experience in data engineering, database development, or related technical roles.
• Strong command of SQL and experience with both relational and non-relational databases (e.g., MongoDB).
• Proficiency in Python (Java or Scala a plus).
• Hands-on experience with workflow orchestration (Apache Airflow) and cloud data warehouses (BigQuery, Snowflake, or Redshift also relevant).
• Experience building dashboards in BI tools such as Looker Studio.
• Familiarity with cloud platforms (GCP preferred; AWS or Azure also valued); exposure to big data tools (Spark, Hadoop, Kafka) is a plus.
• Tech-savvy, with openness to learning and applying AI tools in daily workflows.
• 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

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

Why Join Us?

You’ll be joining a team with a bold mission to transform education. We don’t expect perfection, but we do expect creativity, ownership, and a strong learning attitude. If you’re looking for a conventional engineering role, this may not be the right fit. But if you’re ready to lead with technology, build innovative solutions, and create real-world impact, we’d love to hear from you.