Responsibilities
- Lead and grow a cross-functional data engineering team, promoting technical excellence, accountability, and ongoing improvement.
- Provide mentorship to engineers at all levels through coaching, structured feedback, and clear career development paths.
- Lead global recruitment efforts to attract and retain skilled data engineering professionals.
- Foster a work environment where engineers take ownership and deliver high-impact results with autonomy and speed.
- Oversee the complete data lifecycle, including ingestion, transformation, storage, delivery, and monitoring.
- Contribute technically through system design, code reviews, and solving complex data challenges to set the team's technical standard.
- Establish and uphold best practices in data modeling, pipeline reliability, testing, data quality, and documentation.
- Guide architectural choices for real-time and batch data systems to ensure scalability, maintainability, and cost efficiency.
- Ensure compliance with secure data handling standards such as PCI-DSS and GDPR in line with payments industry requirements.
- Promote an AI-first culture by integrating AI-assisted development, automated testing, and LLM-powered workflows as standard practice.
- Collaborate with Product, Analytics, Machine Learning, Finance, and Compliance teams in an agile setting to execute a dynamic roadmap.
- Act as a liaison between data engineers and data users, ensuring data products are accurate, well-documented, and trusted.
- Advance data infrastructure to support expansion into new markets, payment providers, and evolving regulatory demands.
- Translate business objectives into engineering priorities, balancing speed, reliability, and technical debt management.
Requirements
- Proven experience managing and scaling data or software engineering teams, including hiring, coaching, and performance evaluation.
- Strong capability to guide technical decisions and manage competing demands in a fast-moving environment.
- Excellent communication skills with the ability to interact effectively across technical and non-technical audiences.
- Solid hands-on background in data or software engineering, with experience designing large-scale data pipelines, models, and platforms.
- Proficiency in Python and/or SQL, with familiarity across modern data stack technologies.
- In-depth knowledge of streaming and batch processing systems such as Kafka, Spark, Flink, or Airflow.
- Experience working with cloud-based data infrastructure on AWS, GCP, or Azure, and modern tools like dbt and data lakehouse architectures.
- Understanding of data quality, observability, and governance principles.
- Advocate for AI-driven development, with experience implementing standards for AI-assisted workflows, automated testing, and LLM-based code generation.
- Experience working in agile environments and adapting processes to suit team needs.
- Professional fluency in English, both written and spoken.
Nice to Have
- Background in payments or fintech sectors.
- Familiarity with real-time analytics, event-driven systems, and high-volume transaction data.
- Experience with machine learning platform design or feature store implementations.
- Knowledge of DevOps practices in data contexts, including CI/CD for pipelines, infrastructure as code, and data contracts.
Benefits
- Competitive Compensation
- Remote Work – You can work from everywhere!
- Home Office Bonus – A one-time allowance to help you create your ideal home office
- Work Equipment
- Stock Options
- Health Plan wherever you are
- Flexible Days Off
- Language, Professional, and Personal Growth courses
Compensation
Competitive Compensation
Work Arrangement
Remote Work – You can work from everywhere!
Team
Multidisciplinary data engineering team focused on scalable data systems and AI-first practices
Other
Professional proficiency in English — written and spoken.
