AIFT

Virtual Insurance

SeniorDataEngineer

Taiwan
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior Data Engineer at AIFT. Skills: data engineering, data platform architecture, AI/ML engineering, modern cloud data platforms, BI data foundations, GenAI / LLM architectures, SQL, workflow orchestration tools, streaming platforms, pipeline design, data warehouse development, dimensional modeling, data governance, data lineage, data privacy, data security. own day-to-day operations of data platforms/pipelines capacity, stability, upgrades, deployments, and recovery drills. design/manage mult”

Industry & Context.

Virtual Insurance
Problems you'll solve

problem-solving skills; debugging; performance tuning

What They're Looking For.

Must Have

Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field, 5+ years of experience in data engineering, data platform architecture, or AI/ML engineering, Proficiency in SQL, workflow orchestration tools (e.g., Airflow), streaming platforms (e.g., Kafka), pipeline design best practices, Solid understanding of data warehouse development lifecycles, dimensional modeling concepts, debugging skills, performance tuning skills, problem-solving skills, Working knowledge of data governance, lineage, privacy, security frameworks

Nice to Have

modern cloud data platforms (e.g., Snowflake, Databricks, BigQuery, Redshift), Hands-on experience building BI data foundations, supporting GenAI / LLM architectures, Familiarity with GitLab, CI/CD pipelines

What You'll Do.

own day-to-day operations of data platforms/pipelines capacity

design/manage multi-source ingestion

Develop rule-based and statistical data quality checks

Implement automated remediation

reconciliation workflows

and historical backfilling

Establish monitoring and alerting frameworks

plan and maintain scalable ETL/ELT including scheduling

Enforce data access controls

Apply Infrastructure-as-Code

Contribute to embedded GenAI and LLM-powered data applications

Partner with analytics and product teams to operationalize AI-driven data solutions

How You'll Work.

Team & Collaboration

Partner with analytics and product teams

Full Job Description

[Job Overview] We are looking for an experienced Senior Data Engineer to join our engineering team and play a key role in building and scaling our enterprise data platform. You will design, develop, and maintain high-quality data warehouses and data-driven applications that power analytics, reconciliation, and business decision-making across the organization. This role requires strong expertise in modern data architectures, pipeline engineering, and data quality management. The ideal candidate combines hands-on technical capability with a deep commitment to reliability, scalability, and governance in a regulated environment. [Responsibilities] · Data operations: own day-to-day operations of data platforms/pipelines capacity, stability, upgrades, deployments, and recovery drills to sustain high availability and low latency. · Data collection: design/manage multi-source ingestion (exchanges, internal and external systems), protocol parsing, and robust retry mechanisms. · Develop rule-based and statistical data quality checks (completeness, uniqueness, time alignment, anomaly detection, error handling). · Implement automated remediation, reconciliation workflows, and historical backfilling. · Establish monitoring and alerting frameworks to ensure trusted, production-grade datasets. · End-to-End pipelines: plan and maintain scalable ETL/ELT including scheduling, caching, partitioning, modelling, schema evolution, and lineage to support both batch and real-time streaming. · Enforce data access controls, encryption, auditing, and classification to comply with internal policies and external regulatory requirements (including PII management). · Apply Infrastructure-as-Code, data versioning, data tests, and CI/CD to improve predictability and reduce manual risk. · Contribute to embedded GenAI and LLM-powered data applications for enterprise analytics, reconciliation, and internal productivity use cases. · Partner with analytics and product teams to operationalize AI-driven d

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