Data Engineer Head - Zurich Asuransi Indonesia

Job Summary

Leads the data engineer team(-s) in the development of distinctive analytical and risk insights that deliver improvements in business results through the application of advance analytical techniques to complex data sets.

Job Qualifications

• 7+ years’ experience in Data Engineering with SparkSQL, Python and PySpark for the workflow.
• Prove track record in leading data engineer team.
• Strong proficiency in dimensional modeling and star schema design for analytical workloads.
• Experience implementing automated testing and CI/CD pipelines for data workflows.
• Familiarity with GitHub operations and collaborative development practices.
• Demonstrated ability to optimize engineering workflow jobs for performance and cost efficiency.
• Experience with cloud data services and infrastructure (AWS, Azure, or GCP).
• Proficiency with IDE tools such as Visual Studio Code for efficient development.
• Experience with Databricks platform will be a plus.

Job Accountabilities - Key Accountabilities

Leadership Capabilities

• Leads the development of next-generation market leading analytic techniques to build the data engineering capabilities across the organization. 
• Complete in-depth diagnostics of project needs issues and business drivers and recommend process and system improvement to enhance customer service.
• Develop propose and lead the implementation of data initiatives to improve processes develop policies and generate innovation to maintain regulatory compliance andor deliver process or product enhancements.
• Establish maintain and grow strong collaborative relationships with key internal and external partners leveraging relationships to drive ongoing improvements to the organizations data strategy.

Technical Capabilities

• Design and implement ETL/ELT pipelines using Spark SQL and Python within Databricks Medallion architecture.
• Develop dimensional data models following star schema methodology with proper fact and dimension table design, SCD implementation, and optimization for analytical workloads.
• Optimize Spark SQL and DataFrame operations through appropriate partitioning strategies, clustering and join optimizations to maximize performance and minimize costs.
• Build comprehensive data quality frameworks with automated validation checks, statistical profiling, exception handling, and data reconciliation processes.
• Establish CI/CD pipelines incorporating version control, automated testing including but not limited to unit test, integration test, smoke test, etc.
• Implement data governance standards including row-level and column-level security policies for access controls and compliance requirements.
• Create and maintain technical documentation including ERDs, schema specifications, data lineage diagrams, and metadata repositories.