Highlights
- Fixed bonus and performance bonus
- Hybrid Working and remote teaming
- Work from Anywhere
- Equality Welfare & Benefit (LGBTQA+)
- Global online training modules to personalized learning journey
- International Opportunities for Career Growth
- Community Support with Employee well-being resource groups
At Generali Thailand
We celebrate diversity and believe that different perspectives make us stronger. For this role, strong Thai communication skills are essential because you will work closely with our local team and collaborate on daily operations.
We are looking for someone who can:
- Speak and understand Thai confidently (reading and writing are not required)
- This requirement ensures smooth communication and effective teamwork. While we embrace diversity in all other aspects, this language skill is critical to the success of the role.
About the Role
We are seeking an experienced Technical Lead Data Engineer to lead the transformation of our Enterprise Data Solutions and accelerate our cloud modernization journey. This role will lead a team of Data Engineers while remaining hands-on in designing, building, and delivering scalable, secure, and well-governed data platforms that support applications, analytics, data science, and AI initiatives. The successful candidate will bring strong expertise in Enterprise Data Architecture, Data Governance, Application Integration, and modern cloud-based data engineering practices.
Key Responsibilities
Team Leadership & Capability Building
- Act as both technical authority and mentor for a team of Data Engineers.
- Provide technical direction, architecture guidance, and engineering standards.
- Conduct code reviews, technical coaching, and career development planning.
- Identify skill gaps and establish learning pathways in cloud engineering, data architecture, automation, and modern data engineering practices.
- Foster a culture of continuous learning, innovation, collaboration, and accountability.
- Guide engineering teams while directly contributing to solution design, coding, and resolving complex technical challenges.
Data Platform Modernization & Cloud Migration
- Lead the migration of enterprise data platforms from on-premises environments to cloud platforms in alignment with the enterprise roadmap.
- Design scalable, secure, resilient, and cost-effective cloud data platforms.
- Establish infrastructure automation, CI/CD pipelines, monitoring, and operational governance.
- Build scalable data serving layers, semantic models, and data marts.
- Ensure platform availability, scalability, disaster recovery, and operational excellence.
- Deliver accessible, reliable, well-governed, and business-ready data assets.
- Implement data quality controls, reconciliation processes, and automated monitoring.
- Improve end-to-end data freshness and reduce delivery latency for reporting, analytics, and AI use cases.
Enterprise Data Architecture & Governance
- Translate enterprise data architecture principles into practical and scalable engineering solutions.
- Apply enterprise data architecture standards across platforms and business domains.
- Define and maintain data models, data standards, metadata structures, and integration patterns.
- Embed governance controls, lineage tracking, and metadata management throughout the data lifecycle.
- Establish and enforce data ownership, stewardship, and data quality practices across domains.
- Ensure compliance with PDPA, internal security policies, audit requirements, and corporate governance standards.
- Implement access controls, data masking, and encryption standards aligned with enterprise and group security policies.
- Drive adoption of modern architecture approaches, including Data Lakehouse and event-driven patterns, where appropriate.
Application & Enterprise Integration
- Build seamless data exchange pipelines between cloud data platforms and transactional business applications.
- Design and implement integrations across core systems, CRM platforms, customer applications, and external data providers.
- Implement modern integration approaches, including APIs, CDC, event streaming, and batch processing.
- Ensure secure, scalable, low-latency, and reliable data movement across enterprise systems.
Key Impact
- Enable high-quality, AI-ready data ingestion at scale, reducing time-to-market for enterprise AI capabilities.
- Establish a secure and compliant data foundation that protects enterprise data privacy during analytics and AI interactions.
- Eliminate critical production data bottlenecks while improving performance, latency, and reliability for enterprise RAG and search workloads.
- Support the evolution of traditional data warehouse and data lake environments toward graph-enabled and vector-enabled AI-ready data architectures where appropriate.
Qualifications / Requirements
Experience
- Bachelor's or Master's degree in Computer Engineering, Computer Science, or a related technical field.
- 7+ years of experience in Data Engineering, including at least 3 years in senior or technical leadership roles.
- 3+ years of hands-on experience leading Data Engineering teams.
- Proven experience architecting and operating cloud data platforms at production scale.
- Demonstrated success delivering cloud migrations, platform transformations, or large-scale data platform implementations.
- Experience leading enterprise-wide initiatives such as technical debt reduction, process improvements, or metrics standardization.
- Experience working in complex, matrixed organizations with multiple cross-functional stakeholders.
Technical Expertise & Hands-On Capability
- Expert-level Python development with proven experience building enterprise-scale data frameworks and reusable pipeline components.
- Comfortable writing production-grade code and actively contributing hands-on rather than providing architecture guidance only.
- Deep understanding of:
- Distributed systems architecture and design trade-offs
- Data warehousing, data lakes, and enterprise data architectures
- ETL/ELT frameworks and streaming data platforms
- Data quality frameworks and validation methodologies
- Cloud cost optimization strategies
- Strong expertise in Infrastructure as Code (IaC), container technologies, CI/CD pipelines, and data orchestration platforms.
- Strong database design, optimization, and performance tuning across SQL and NoSQL technologies.
- Proven ability to troubleshoot and resolve complex distributed systems issues independently.
- Strong awareness of emerging technologies and best practices within modern data engineering.
Leadership & Soft Skills
- Demonstrated ownership mindset with strong execution capabilities.
- Proactive problem solver who approaches challenges with a technical and solution-oriented mindset.
- Strong mentoring, coaching, and team development skills.
- Excellent communication skills with the ability to translate technical concepts into business and executive language.
- Able to thrive in fast-paced environments with evolving priorities and ambiguity.
- Creates a psychologically safe environment that encourages collaboration, experimentation, and accountability.
- Proven ability to align stakeholders and drive outcomes across competing priorities.
Mandatory Candidate Requirements (Must Have)
Candidates must demonstrate current, hands-on experience in the following areas:
- Current production experience using Python, PySpark, and/or Apache Spark.
- Experience building end-to-end data pipelines (ETL/ELT or similar) in production environments.
- Hands-on experience with AWS data services or equivalent cloud data platforms.
- Experience implementing CI/CD practices, Git, Bitbucket, and DevOps methodologies within data engineering projects.
- Experience integrating data through APIs, CDC, Kafka, Event Hub, or similar integration technologies.
- Significant hands-on coding responsibilities in their current role, with the ability to clearly articulate the percentage of time dedicated to coding.
- Experience leading, mentoring, coaching, or providing technical guidance to a team of Data Engineers.
