Senior Data Engineer

• Collaborate with Agile product teams (mobile, digital banking, corporate banking, and customer insight) to design, develop, test, implement, and support full- stack data solutions that directly impact millions of mobile banking users
• Partner closely with mobile app teams, data scientists, and business stakeholders to translate digital banking requirements into production- grade data products
• Build high- throughput, low- latency real- time and batch data pipelines to power behavioral analytics, next- best- action recommendations, and seamless customer experiences
• Ensure enterprise- grade data quality, governance, lineage, security, and compliance in a highly regulated environment
• Create scalable ingestion, transformation, quality enforcement, and serving layers that feed our Next- Gen Mobile Banking App and other Digital Banking platform products
• Mentor junior and mid- level data engineers, conduct design reviews, and leverage DataOps best practices (CI/CD, automated testing, observability)
• Optimize pipelines for performance, cost- efficiency, reliability, and scalability on Google Cloud Platform (GCP)
• Design and evolve our enterprise Data Platform with Data Vault 2.0 as the foundational modeling methodology while delivering high- quality, trusted data layers in Google Cloud

• Proficiency in dbt for data transformations, Python/PySpark development, and modern data warehouse/lakehouse approaches
• Expertise in scalable data modeling — Data Vault 2.0 strongly preferred (or demonstrated success delivering agile, auditable enterprise data models such as medallion or lakehouse patterns)
• Practical experience with real- time streaming technologies (Spark Streaming, Flink, Kafka Streams) and workflow orchestration (Airflow)
• Bachelor’s Degree in Computer Science, Engineering, or a related field
• Experience leveraging AI copilots and hands- on familiarity with AI agents (e.g., GitHub Copilot, Claude Code, OpenClaw, ...) to accelerate development, code review, and pipeline automation
• Familiarity with data governance, lineage practices, and compliance requirements in a regulated banking environment
• Solid experience (typically 5+ years equivalent) in data engineering using Python, SQL, Spark / PySpark/
• Experience designing, developing, and optimizing RESTful APIs and/or GraphQL APIs (including schema design, performance tuning, versioning, security, rate limiting, and integration with mobile/web frontends or microservices)
• Proven hands- on experience with Google Cloud Platform (GCP), particularly BigQuery as a core data warehouse/lakehouse solution
• Strong background in building and operating big data pipelines, including real- time/near- real- time processing with Spark, Spark Streaming, or equivalent frameworks
• Solid understanding of digital banking data domains (T24 data, mobile transactions, banking products, app events, customer behavioral, corporate banking flows)

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Cập nhật gần nhất lúc: 2026-03-30 13:25:03