Roles and Responsibilities:
Design and build end-to-end data pipelines – From defining source structures and integrating APIs to delivering clean, trusted datasets that enable analytics, reporting, and operational workflows.
Translate business needs into scalable data solutions based on business priorities and the product roadmap, understand technical requirements, and deliver purpose-built pipelines and tools.
Act as the technical partner for Product, Finance, Operations, and R&D teams, translating business needs into scalable data models, pipelines, and internal tools while ensuring reliable, trusted data across the organization.
Write high-quality, maintainable code, while following best practices and leveraging modern data tooling and CI/CD principles.
Drive data quality and reliability – Monitor, validate, troubleshoot, and continuously improve data quality and pipeline reliability across the data platform.
Requirements:
B.A / B.Sc. degree in a highly quantitative field.
4+ years of hands-on experience in data engineering, building data pipelines, writing complex SQL, and structuring data at scale.
Fast learner with high attention to detail, strong ownership, and the ability to manage multiple priorities in a dynamic environment.
Strong communication skills with the ability to partner effectively with both technical and business stakeholders.
Experience with Google Cloud data technologies (BigQuery, Cloud Composer/Airflow, Pub/Sub, Cloud Functions) or equivalent AWS/Azure data services.
Hands-on experience with dbt for data transformation and modeling.
Practical experience using AI tools (e.g., Claude, Cursor, GitHub Copilot, etc.) to improve development workflows and productivity.
Experience building internal data tools, automation workflows, or AI-powered solutions.
High business intuition and analytical mindset, with a strong sense of how to turn raw data into insights and impact.
Fluent English and experience working with global teams.
Nice to Have:
Experience in designing and building scalable data systems for various data applications.
Background in data-driven companies in large-scale environments.
