Data Engineer
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A leading financial institution is seeking a Senior Data Engineer to build scalable lakehouse platforms, governed data products and AI‑ready data assets in close collaboration with architecture, governance, analytics and business stakeholders.
You will deliver batch & real‑time data pipelines, enforce data quality and observability, and partner with AI/data science teams to power NLP, RAG and agent‑driven analytics for banking risk and fraud use‑cases.
Responsibilities: • Design metadata‑driven ingestion frameworks for structured and unstructured data, covering batch, CDC, streaming, API and file sources • Develop reusable Spark / Java / Python components; build Bronze‑Silver‑Gold lakehouse layers and implement data modelling, lineage, SLA‑enabled data products • Tune distributed workloads, implement data quality controls and production observability • Apply DevOps, CI/CD and IaC on Docker / Kubernetes; operationalise ML models alongside data scientists • Architect data foundations supporting RAG, agent‑AI and multi‑modal unstructured‑data workflows • Build internal engineering tooling and align data implementations with financial regulatory requirements • Troubleshoot production workloads and drive platform reliability improvements
Requirements: • 10+ years hands‑on data‑engineering delivery experience, banking / financial services background preferred • Strong skills in PySpark, Java, Python, advanced SQL; solid knowledge of data modelling, lakehouse architecture, ETL/ELT and CDC • Practical experience building ingestion pipelines and data products; working familiarity supporting RAG / NLP / agent‑AI workloads • Hands‑on with Docker, Kubernetes, CI/CD and observability tooling • Able to translate business & risk requirements into data‑platform designs; good cross‑team communication • Bachelor / Master's in Computer Science or quantitative‑related discipline Desirable: • Airflow, Terraform and cloud‑native data‑platform experience • Experience with Iceberg, Kafka, Flink, NiFi or Cloudera Machine Learning
Please contact Xuemin at (HIDDEN TEXT) for a discussion. EA License no.: 16S8066 Rep no.: R25157345 Only successful applicants will be notified.
Responsibilities: • Design metadata‑driven ingestion frameworks for structured and unstructured data, covering batch, CDC, streaming, API and file sources • Develop reusable Spark / Java / Python components; build Bronze‑Silver‑Gold lakehouse layers and implement data modelling, lineage, SLA‑enabled data products • Tune distributed workloads, implement data quality controls and production observability • Apply DevOps, CI/CD and IaC on Docker / Kubernetes; operationalise ML models alongside data scientists • Architect data foundations supporting RAG, agent‑AI and multi‑modal unstructured‑data workflows • Build internal engineering tooling and align data implementations with financial regulatory requirements • Troubleshoot production workloads and drive platform reliability improvements
Requirements: • 10+ years hands‑on data‑engineering delivery experience, banking / financial services background preferred • Strong skills in PySpark, Java, Python, advanced SQL; solid knowledge of data modelling, lakehouse architecture, ETL/ELT and CDC • Practical experience building ingestion pipelines and data products; working familiarity supporting RAG / NLP / agent‑AI workloads • Hands‑on with Docker, Kubernetes, CI/CD and observability tooling • Able to translate business & risk requirements into data‑platform designs; good cross‑team communication • Bachelor / Master's in Computer Science or quantitative‑related discipline Desirable: • Airflow, Terraform and cloud‑native data‑platform experience • Experience with Iceberg, Kafka, Flink, NiFi or Cloudera Machine Learning
Please contact Xuemin at (HIDDEN TEXT) for a discussion. EA License no.: 16S8066 Rep no.: R25157345 Only successful applicants will be notified.