Data Engineer

Bowtie Life Insurance Company

Data Engineer developing self-serve BI, data warehouses, and dashboards for health insurance disruptor Bowtie. Collaborating across teams to shape analytics foundations and grow data capabilities.

Posted 6/18/2026full-timeHong Kong • 🇭🇰 Hong KongMid-LevelSeniorWebsite

ATS Keywords

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Hard Skills

Data ModellingSQLELTReverse-ELTCI/CDdata transformation toolspredictive modelingmachine learningdata workflowsdata analysis

Soft Skills

collaborationlisteningconsolidation of business requirementsdecision-making facilitationuser story analysis

Tools & Technologies

AWSdbtAirflowDagsterPrefectMetabaseLookerTableauPower BIOpenMetadata

Industry Keywords

business intelligencedata infrastructurepredictive analyticsreporting systemsdata warehouses

Tech Stack

Tools & technologies

AirflowAWSCloudSQLTableau

About the role

Key responsibilities & impact

Collaborate hands-on with backend engineers and business users to architect and maintain a secure self-service BI solution, including data warehouses, dashboards, and various reporting systems
Produce analysis for internal business users
Help build the foundation for emerging data-science capabilities, supporting predictive workflows and personalised customer experiences
Ingest and consolidate data from various sources into a single source of truth using modern ELT (Extract, Load, Transform) and Reverse-ELT pipelines
Design technical strategies and infrastructure to facilitate self-service business intelligence, including strategies to deploy data warehouse changes with CI/CD pipelines
Design dashboards for business users to monitor trends and patterns
Collaborate on setting up the data infrastructure needed for machine learning models, predictive analytics, or forecasting
Prepare automated reports for strategic projects and regulatory reporting
Assist business users to obtain insights and facilitate decision-making

Requirements

What you’ll need

[proficient] Data Modelling techniques using SQL
[proficient] Eager to work closely with both the engineering team and business users, listen to user stories, and consolidate business requirements
[proficient] Good understanding of software engineering practices
[preferable] High-level knowledge in cloud-based infrastructure and services (preferably in AWS)
[fair] Experience with modern data transformation tools (e.g. dbt) and data workflow orchestrators (e.g. Airflow, Dagster, Prefect)
[fair] Experience using modern BI and analytics platforms (e.g. Metabase, Looker, Tableau, Power BI) and familiarity with metadata catalogs or data lineage tools (e.g. OpenMetadata, DataHub)
[preferable] Exposure to (or curiosity about) basic statistics, predictive modeling, or setting up data layers for ML/AI applications.

Benefits

Comp & perks

Professional development opportunities