Data Engineer

Role overview Design and operate data infrastructure for a regulated financial environment. You’ll shape a configuration-driven framework on Databricks and a Snowflake warehouse, while owning dependable pipelines built to support auditability, traceability, and rigorous data controls.

Responsibilities - Design and evolve the data framework and warehouse architecture, making and explaining technical trade-offs. - Build and own pipelines using Python and PySpark, with event-driven components such as Kafka. - Ensure pipelines are idempotent, resilient, auditable, and aware of data lineage. - Implement data quality practices covering testing, validation, monitoring, and alerting. - Lead technical discussions and design reviews, and coach junior engineers. - Work with Product, Engineering, DevOps, and Regulatory teams to turn complex requirements into scalable designs delivered in incremental steps.

Requirements - Senior-level data engineering experience and ability to take ownership of architecture and delivery. - Practical experience with Databricks, Snowflake, Python, and PySpark. - Understanding of event-driven data components such as Kafka. - Ability to build reliable, auditable pipelines with lineage, idempotency, and resilience in mind. - Experience establishing data quality controls, including automated tests, validation, monitoring, and alerting. - Clear communication, design review leadership, and the ability to mentor engineers and collaborate across technical and regulatory teams.