Location: Remote - Europe

Remote | Full-time

Compensation: $110K - $125K

We are hiring on behalf of our client who is a premier infrastructure provider within the decentralized web (Web3) ecosystem, serving as the critical connectivity layer for tens of thousands of applications and millions of users globally. Having established a dominant market position, the organization is now expanding its horizons by launching a sophisticated payments solution.

This new initiative enables merchants and payment providers to utilize blockchain rails for stablecoin checkouts, payouts, and deposits, already reaching millions of terminals worldwide through strategic global partnerships.

The data systems supporting this infrastructure are foundational to the company's success. As the organization transitions from MVP pipelines to a scalable production architecture, there is a need for a Senior Data Engineer to join the team. This individual will help design and operate the data foundation behind a global payments network, ensuring that systems tracking wallet connections, transaction health, and financial reconciliations are as reliable as the payment flows themselves.

This role sits at the intersection of data engineering and backend systems. The successful candidate will build event-driven pipelines, maintain real-time processing systems, and develop backend services that expose critical data to internal platforms and APIs.

Key Responsibilities

• Data Platform & Pipeline Engineering: Design and operate scalable, near real-time data pipelines for payment and platform data. Evolution of the current architecture from MVP status to a high-availability production environment is a primary focus.
• Financial Data Modeling: Model and process complex transactional and ledger-style data to support financial reconciliation, merchant reporting, and settlement tracking.
• Data Quality & Observability: Ensure the accuracy and freshness of data across critical workflows. This includes building robust monitoring and alerting systems that meet the stringent reliability standards of financial infrastructure.
• Backend & API Development: Build and maintain backend services to expose data to internal platforms and downstream consumers, ensuring clean integration points for product and engineering teams.
• Operational Ownership: Own systems end-to-end, from initial deployment to ongoing monitoring and reliability. This includes contributing to engineering standards and operational playbooks within a remote-first, asynchronous environment.

Requirements

Minimum Qualifications:

• Professional Experience: 5+ years of experience in data engineering or data infrastructure, with a proven track record of shipping and operating production-grade systems.
• Technical Proficiency: Expert-level SQL skills and strong programming experience in Python or similar backend languages.
• Modern Data Stack: Significant experience with tools such as Airflow, dbt, ClickHouse, BigQuery, Snowflake, or Athena.
• Architectural Knowledge: Deep understanding of event-driven architectures and real-time/near real-time data processing.
• AI Integration: Proficiency in using AI-assisted development tools and agents, maintaining high discipline in validating and productionizing generated code.
• Reliability Mindset: Experience designing systems where data quality and observability are treated as first-class concerns.

Preferred Qualifications:

• Sector Expertise: Previous experience in Fintech, payments, or financial infrastructure (specifically reconciliation, settlement, or ledger reporting).
• Industry Context: Exposure to Web3, blockchain, or crypto ecosystems (on-chain data or wallet analytics).
• System Scale: Experience handling high-volume event data at scale within a fast-growing startup environment.
• Languages: Familiarity with Rust is considered a strong plus.

Benefits

• Remote-First Culture: Fully remote position with a dedicated budget for home office setup.
• Global Engagement: Opportunities for regular team offsites at international locations and travel to industry conferences.
• Professional Growth: Meaningful Learning & Development budget to support continuous skill acquisition.
• Comprehensive Leave: Generous Paid Time Off (PTO) and parental leave policies.
• Competitive Package: Compensation includes salary and equity components.
• Healthcare: Coverage provided for US-based team members.



Interview Process

1. Initial Screening: A preliminary call with HR/Recruitment.
2. Hiring Manager Interview: A 45-minute technical deep dive with the Data Engineering Lead. This session focuses on past systems, data pipelines, architecture trade-offs, and experience with transactional data.
3. Technical Assessment: A two-part interview covering:

• System Design: Focused on data architecture and high-volume pipelines.
• Practical Assessment: A problem-solving exercise focused on logic and approach.

1. Final Interview: A conversation with senior leadership to assess ownership, communication style, and alignment with a high-autonomy, remote engineering culture.