What you'll be doing

• Bridge Engineering & Analytics by transforming raw system inputs into clean, aggregated data models for Product Analysts and Data Scientists.
• Modernise Verification Data Architecture by building and standardising production pipelines using Python and Airflow.
• Improve Performance & Scale by optimising data workflows, resolving latency bottlenecks, and ensuring data consistency across onboarding domains.
• Set Data Engineering Standards through partnership with engineering, analytics, and compliance to define data contracts, pipeline monitoring, and automated testing frameworks.

Requirements

• Strong Python fundamentals with the ability to write clean, maintainable, and efficient code.
• Advanced SQL and data modelling skills, including designing normalized and dimensional data models and tuning complex queries.
• Experience building, scheduling, and monitoring production pipelines using workflow orchestrators like Apache Airflow.
• Systems and data architecture mindset with experience handling high-throughput event data and understanding analytical databases and storage formats.
• Ownership and pragmatic autonomy to take cross-functional problems from concept to production without hand-holding.
• Excellent collaboration skills to communicate with Software Engineers, Data Scientists, Product Analysts, and stakeholders effectively.

Nice to haves

• 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 with complex event tracking or onboarding funnels (FinCrime or KYC experience not required).