What you'll be doing

Designing and maintaining robust ETL/ELT pipelines for high-volume, event-driven verification data.
Mapping complex transactional events into coherent, business-accurate data models.
Collaborating to modernise legacy verification infrastructure with scalable, flexible data flows.
Ensuring observability and auditability of onboarding flows and FinCrime controls for compliance and monitoring.
Translating raw system inputs into clean, aggregated data models for Product Analysts and Data Scientists.
Building and standardising production pipelines using Python and Airflow.
Optimising workflows and queries to improve performance and scale.
Setting data engineering standards with cross-functional teams on data contracts, monitoring, and automated testing.

Requirements

Strong Python fundamentals with clean, maintainable code and performance optimization skills.
Advanced SQL and data modelling expertise over large-scale datasets.
Experience building, scheduling, and monitoring pipelines using workflow orchestrators like Apache Airflow.
Systems and data architecture mindset with knowledge of high-throughput event data and analytical storage.
Ability to take ownership and work autonomously on cross-functional, evolving problems.
Effective collaboration skills with software engineers, data scientists, analysts, and stakeholders.

Hands-on experience with modern data transformation tools such as dbt.
Familiarity with distributed compute and storage platforms like Kafka, Spark, Snowflake, or Presto/Trino.
Experience in product-led SaaS, fintech, or high-growth tech environments, especially with complex event tracking or onboarding funnels.