Senior Data Engineer, WalletConnect

• 5+ years of experience in data engineering or data infrastructure — you’ve built, shipped, and operated production data systems
• Strong experience building data pipelines and production data platforms — ingestion, transformation, modelling, and serving
• Comfortable using AI-assisted development tools/agents, with strong discipline in reviewing, validating, and productionizing generated code
• Expertise in SQL and large-scale data processing
• Experience with modern data stack tools: Airflow, dbt, ClickHouse, BigQuery, Snowflake, Athena, or similar
• Experience working with event-driven architectures and real-time or near real-time data processing
• Strong programming skills in Python or similar backend languages
• Experience designing systems with data quality, monitoring, and reliability in mind — you treat data correctness as a first-class concern
• Ability to operate independently and own production systems end-to-end in a remote, async team

Desirable

• This is a Senior Data Engineer position on the Data Engineering team. You’ll work closely with the Data Engineering Lead to design and scale the data platform powering WalletConnect’s payments infrastructure and ecosystem analytics
• This role sits at the intersection of data engineering and backend systems. You’ll build and operate event-driven data pipelines, maintain real-time processing systems, and ensure data quality across high-volume transactional workflows
• You’ll also build backend services that expose data to internal platforms and APIs, so teams across the company can consume it
• Day-to-day, you’ll work with Python, SQL, ClickHouse, Airflow, and dbt within an event-driven architecture. You’ll own the systems you build end-to-end — including monitoring, reliability, and data correctness — and collaborate with product, engineering, and infrastructure teams to support new data use cases as the platform grows
• Design, build, and operate scalable data pipelines for WalletConnect payments and platform data — event-driven, near real-time processing across high-volume transaction flows
• Maintain and evolve the real-time data platform, moving it from MVP pipeline to scalable production architecture
• Model and process transactional and ledger-style data — financial reconciliation, merchant reporting, wallet fee breakdowns, and settlement tracking
• Contribute to improving the overall data platform architecture as requirements grow
• Ensure data accuracy, freshness, and observability across critical workflows — payments flow correctness, wallet connection success rates, and transaction health
• Build monitoring and alerting that meets the reliability bar required for financial data systems
• Own data quality across the pipeline — from ingestion through transformation to downstream consumption
• Develop backend services that expose data to internal platforms, APIs, and downstream systems so teams across the company can consume it
• Support downstream use cases including financial reconciliation, merchant reporting, operational monitoring, and product analytics
• Collaborate with product, engineering, and infrastructure teams to onboard new data use cases and ensure clean integration points
• Own and operate the systems you build, including deployment, monitoring, and reliability
• Collaborate closely with the Data Engineering Lead on architecture decisions and platform evolution
• Contribute to engineering standards, documentation, and operational playbooks for the data platform
• Our current stack includes Python, SQL, ClickHouse, Airflow, dbt, Grafana, and Preset / Deepnote for data visualization. The architecture is event-driven with near real-time processing. Rust is a plus but not required. We’re also experimenting with AI-assisted monitoring and data tooling as part of our platform evolution