Data Engineer – Build Reliable Data Pipelines in Finance

About the role

This position is for a Data Engineer based in Ireland, working within a regulated credit and lending environment. You will transform a newly centralized data lake into a robust, analytics‑ready foundation that supports downstream data science and risk modelling use cases.

Key responsibilities

• Rebuild and validate data pipelines to ensure reproducibility of reporting and statistics.
• Profile, reconcile, and harmonise heterogeneous source schemas into a unified data model.
• Design and implement dbt‑based models (staging, intermediate, marts) with extensive testing.
• Develop data quality frameworks using Great Expectations and dbt tests.
• Implement entity resolution and record‑linkage logic across fragmented customer and account data.
• Ensure anonymisation and pseudonymisation meet regulatory requirements.
• Optimise large‑scale Spark jobs and manage cost‑efficient compute usage.
• Orchestrate production‑grade pipelines with Airflow or AWS Step Functions.
• Deliver clean, documented, feature‑ready datasets for data science and risk teams.

Required profile

• 4+ years of professional data‑engineering experience.
• Strong hands‑on expertise with AWS services and Spark.
• Proven ability to design reliable, auditable data pipelines in a regulated environment.
• Experience collaborating with data‑science and engineering stakeholders.

Required skills

• AWS (S3, Glue, Athena, Redshift, EMR, Step Functions)
• Apache Spark
• SQL
• Python
• dbt
• Great Expectations
• Airflow
• Data governance, lineage and reproducibility
• Entity resolution / record linkage
• Anonymisation and pseudonymisation techniques