Data Engineer

About the Company

Our client is a rapidly expanding global fintech organization operating at the convergence of technology and financial services. Valued by millions of users and institutions globally, they emphasize a robust engineering culture focused on ownership, agility, and integrity.

About the Role

We seek a Data Engineer responsible for designing, constructing, and managing essential data pipelines and platform features underpinning business operations. The role involves end-to-end data handling — from ingestion to modeling and downstream consumption — while prioritizing reliability, scalability, and data quality. Additionally, you will leverage modern AI and large language model (LLM) technologies to boost development speed, improve monitoring, and facilitate quicker domain understanding.

Responsibilities

• Develop and sustain production-level data pipelines, handling both batch and real-time processing across ingestion, transformation, and serving stages.
• Take ownership of entire data domains, encompassing business logic, data quality assurance, and incident response beyond mere ticket execution.
• Maintain and build scalable distributed data infrastructures utilizing platforms like Spark, Hadoop, Flink, or comparable big data technologies.
• Implement proactive data quality validations, monitoring systems, and alert mechanisms to detect and resolve issues proactively before affecting downstream processes.
• Incorporate AI and LLM tools to expedite pipeline crafting, documentation, and domain knowledge acquisition.
• Collaborate effectively with cross-functional teams including product management, compliance, platform engineering, and analytics to transform vague requirements into well-defined engineering tasks.
• Enhance platform stability, optimize costs, and promote operational excellence.
• Provide mentorship to junior engineers as expertise deepens.

Qualifications

• Minimum of 4 years experience in data engineering, skilled in building production data pipelines, ETL/ELT processes, SQL, Python, and orchestration frameworks such as Airflow or similar tools.
• Solid grounding in software engineering principles with proficiency in languages like Java, Scala, or Python.
• Experience working with distributed data architectures and handling extensive storage and computation needs.
• Proven capacity to fully own and manage data domains from start to finish.
• Strong aptitude in data modeling emphasizing auditability, lineage tracking, and accuracy.
• Ability to navigate ambiguous or incomplete requirements and translate them into actionable engineering plans.
• Quick learner with demonstrated capability to master new domains effectively.
• Familiarity with applying AI and LLM technologies within data engineering environments is considered a significant advantage.