Data Engineer

2 weeks ago

Manila, National Capital Region, Philippines ECLARO Full time ₱1,200,000 - ₱2,400,000 per year

About the Role:

The
Data Engineer – Reinsurance
will design, develop, and maintain scalable
ETL/ELT pipelines
and
data models
to support analytics, reporting, and integration within the
Property & Casualty (P&C) Insurance
domain. You'll work with
Python, SQL, and AWS Glue
to build efficient, high-performing data workflows and collaborate with cross-functional teams to deliver reliable data solutions.

Required Skills & Qualifications:

Industry Knowledge


• Strong understanding of Property & Casualty (P&C) Insurance domain, data models, and workflows.

Programming & Scripting


• Python: 5+ years of experience with strong proficiency in writing clean, maintainable code.


• Candidates must have experience working with the Pandas library.


• SQL: Advanced SQL skills for querying and transforming structured data within pipelines.

AWS Glue & Related Services


• AWS Glue Jobs: Experience developing and managing Spark or Python Shell-based ETL jobs.


• Glue Crawlers: Proficient in schema inference and metadata cataloging.


• Glue Workflows: Skilled in orchestrating ETL pipelines with jobs, triggers, and crawlers.


• Glue Triggers: Understanding of event-driven, time-based, and conditional execution flows.


• Glue Data Catalog: Experience managing data assets and handling schema evolution.

Data Engineering & Architecture


• Design, develop, and maintain scalable ETL/ELT pipelines for structured and semi-structured data.


• Build and maintain data models to support business intelligence, reporting, and analytics use cases.

Data Integration & Management


• Integrate data from APIs, databases, file systems, and third-party platforms into centralized data lakes or warehouses.


• Manage and optimize relational and non-relational databases for high performance and reliability.

Performance & Optimization


• Monitor, profile, and optimize ETL workflows and queries for efficiency and scalability.


• Collaborate on cost-effective, reliable data infrastructure design and implementation.

Cross-functional Collaboration


• Partner with data scientists, analysts, product managers, and engineers to deliver integrated, data-driven solutions.

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