Middle Data Engineer in fintech

Plazo Technologies operates in fintech and lending, providing data-driven services across risk, collections, marketing, finance, and data science.

• Design, build, and optimize data models and transformation pipelines in a ClickHouse-based warehouse;
• Develop and maintain ETL/ELT ingestion from operational databases, event streams, and third-party APIs;
• Improve the performance, stability, and cost of existing pipelines and queries as data volumes grow;
• Own data quality through automated tests, freshness and anomaly monitoring, and investigation of issues;
• Work directly with Risk, Collections, Marketing, Finance, and Data Science teams to turn business questions into reliable data models;
• Support analysts and BI users and reduce recurring manual requests by productising them.

• 3+ Years of hands-on experience as a Data Engineer, including work in a production analytical warehouse;
• At least 1 year of practical ClickHouse experience, including table engines, partitioning, materialized views, and diagnosing slow or memory-heavy queries;
• Strong SQL, including window functions, array functions, complex joins, and incremental logic on large tables;
• Confident Python for data processing, integration, and automation;
• Practical Apache Airflow experience, including building and debugging DAGs, retries, and backfills;
• Understanding of streaming and CDC-based replication using Kafka, Debezium, or equivalent;
• Practical dbt experience with incremental models, tests, and project structure;
• English at intermediate level or above;
• Nice to have: BI tools such as Tableau or Apache Superset, data quality or observability frameworks, fintech or lending background, data modelling and DWH architecture in column-oriented databases, Spanish.

• No conditions specified