1) About the company
Our client is an established multinational insurance corporation (MNC) with a formidable footprint across the Asia-Pacific region. Renowned for its customer-centric approach, financial strength, and continuous commitment to digital transformation, the organisation heavily invests in modernising its data infrastructure and advanced analytics capabilities to power intelligent underwriting, claims processing, and personalized insurance solutions.
2) About the job
As an Analytics Engineer, you will sit at the intersection of business logic, software engineering, and data analytics. Your primary mission is to transform raw data into reliable, query-ready models and scalable visual assets.
Key responsibilities include:
- Designing, developing, and optimizing scalable ELT/ETL pipelines to feed modern Data Lakehouse and Data Warehouse architectures.
- Translating complex insurance domain logic into structured dimensional models, data marts, and clean datasets.
- Building and managing high-impact, interactive dashboards and visual reports using Power BI and Tableau for senior management and cross-functional teams.
- Collaborating with data engineers, product owners, and business analysts to uphold high standards of data governance, testing, and documentation across the data lifecycle.
- Work Arrangement: 3 days in-office, 2 days remote (3+2 hybrid schedule).
- Perks & Coverage: Comprehensive corporate medical insurance covering both outpatient and hospitalisation for you and your dependents.
3) About the team
You will join a dynamic, collaborative, and fast-growing Data & Analytics chapter composed of data engineers, BI developers, data scientists, and business translators. The team operates under an agile framework, championing a modern data stack, code versioning, automated testing, and a culture of continuous upskilling.
4) Short summary of the role (the hook)
Bridge the gap between raw data engineering and executive decision-making. If you are skilled in turning complex data lakehouse layers into clear, production-ready BI models and dashboards within an enterprise MNC environment, this is your next career step.
5) Education
- Bachelor’s or Master’s Degree in Computer Science, Data Science, Information Technology, Software Engineering, Statistics, or a related quantitative discipline.
6) Qualifications
- Minimum 3–6 years of hands-on experience in an analytics engineering, data engineering, or advanced BI capacity.
- Proven track record of architecting, deploying, and maintaining data models within modern Data Warehouse or Data Lakehouse ecosystems.
- Demonstrated experience delivering enterprise-grade reporting and dashboard solutions (Power BI / Tableau) to non-technical and executive stakeholders.
- Strong familiarity with data modelling techniques (e.g., Kimball methodology, star/snowflake schemas, dimensional modelling).
- Exposure to the insurance or broader financial services (FSI) sector is an added advantage.
7) Key skills required
- Core Languages: Advanced SQL (query optimization, window functions, CTEs) and Python for data transformation, scripting, and automation.
- Business Intelligence & Visualisation: Hands-on mastery of Power BI and/or Tableau, including DAX, data prep, row-level security, and performance tuning.
- Architecture & Pipelines: Strong fundamentals in ETL/ELT pipeline design, Data Warehousing, and Data Lakehouse design patterns.
- Preferred Modern Stack Tools:
- Platforms: Microsoft Fabric, Databricks, Snowflake
- Orchestration: Apache Airflow
- Software Practices: Version control with Git, CI/CD data workflows, and automated testing frameworks.
