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Payrails is a global payment software company backed by top-tier investors (Andreessen Horowitz, HV Capital, EQT Ventures, General Catalyst) that helps enterprises manage and optimize their payment operations. The company is building a deeply integrated meta layer spanning the entire payment lifecycle with a modular architecture.
As a Senior Analytics Engineer, you will be a core member of the data team, responsible for building and maintaining the data infrastructure that powers decision-making across the organization. Your impact will span the entire data lifecycle—from warehouse architecture to self-serve analytics enablement.
Key responsibilities include:
- Develop and maintain the data warehouse and curated, analytics-ready datasets used company-wide
- Own and evolve the dbt codebase, applying best practices in modularity, testing, and documentation
- Integrate third-party data sources (HubSpot, GitHub, Linear, etc.) into the data ecosystem to enrich reporting and analytics
- Build and maintain tooling and infrastructure to improve the development experience for analytics and business users
- Partner with analysts and stakeholders to design and implement scalable data models supporting self-serve analytics
- Advocate for data engineering and analytics best practices, fostering a culture of data trust, documentation, and reproducibility
- Champion data democratization by ensuring teams have the right tools and trusted data to make informed decisions
You will work in a high-impact, high-velocity environment alongside talented engineers tackling complex problems that affect customers globally. The role offers real ownership, the freedom to build and lead, and the opportunity to shape a company and category from the ground up.
The position is hybrid with an office in New Cairo. The company offers 27 days of annual paid vacation, competitive salary and equity, and regular team events and off-sites.
REQUIREMENTS:
- Proven experience in analytics or data engineering roles with a strong focus on enabling downstream analytics and reporting
- Strong proficiency in Python for scripting, tooling, and data transformation tasks
- Hands-on experience with dbt and SQL-based data transformation pipelines
- Deep understanding of data modeling principles (dimensional modeling, star/snowflake schema)
- Familiarity with modern OLAP data warehouses such as Snowflake (preferred), BigQuery, or Redshift
- Experience working with orchestration tools such as Airflow or similar
- Familiarity with cloud platforms (AWS, GCP, or Azure) and containerized environments (Docker; Kubernetes is a plus)
- Excellent communication skills and ability to work cross-functionally with technical and non-technical stakeholders
- Self-starter mindset with a passion for clean, documented, and tested data code
