GCP Application Technical Specialist

Data & MLOps Engineer (Transaction Monitoring)

Role Purpose

Transaction Monitoring is evolving from traditional rule-based detection towards data-driven and AI-powered solutions that improve risk coverage, investigator effectiveness, and operational efficiency. We are looking for a Data & MLOps Engineer to help evolve our transaction monitoring analytics engineering and MLOps capabilities on Google Cloud Platform (GCP). You will design, build, and operate scalable data and machine learning solutions, while helping the team strengthen its GCP engineering expertise. The role combines Data Engineering, MLOps, and Cloud Engineering, ensuring that data products and machine learning models can be developed, deployed, monitored, and operated at scale.

Key Responsibilities

Build and maintain data pipelines using SQL (on BigQuery, using dbt), Spark, Airflow. Develop cloud-native data solutions on GCP. Operationalise, monitor, and support machine learning models in production. Implement data quality, governance, lineage, and monitoring controls. Collaborate with Data Scientists and Engineers to deliver reliable, scalable solutions. Drive adoption of engineering best practices across Data Engineering and MLOps. Act as a GCP champion and support the team's growth in cloud capabilities Skill Requirements

Required Skills

Technical

SQL

dbt (Data Build Tool) BigQuery Apache Spark Apache Airflow Google Cloud Platform (GCP)

Experience

Data processing and large-scale data platforms Data governance and quality management Data quality monitoring and automated controls Machine learning and MLOps practices Operating and monitoring ML models in production

Nice to Have

Soda Data Quality Feature stores and model monitoring frameworks Financial Crime, AML, KYC, or Transaction Monitoring experience What Success Looks Like Reliable and scalable data pipelines running on GCP. Robust monitoring and data quality controls embedded in the platform. Machine learning models deployed and operated efficiently in production. Improved engineering standards and cloud adoption across the TM MLOps team(s) Increased GCP, Data Engineering, and MLOps maturity across the team through knowledge sharing, coaching, and establishment of best practices.