The Role
Design, build, and maintain scalable data pipelines and infrastructure for extraction, transformation, and loading. Collaborate with stakeholders to deliver analytics tools, optimize data delivery, enforce security and retention policies, and support data scientists and analytics teams.
Summary Generated by Built In
Role Summary
- Design, build, and maintain scalable data solutions that support reporting, self-service analytics, and advanced analytics.
- Work primarily on the organization’s data platform, currently centered on Snowflake and IICS, to transform raw data into trusted, business-ready data assets.
- Partner closely with Data Analysts and source-system teams to translate business needs into data models, pipelines, and reusable data products.
- Use AI-assisted development practices across the full delivery lifecycle to improve design quality, development efficiency, testing, documentation, and technical communication.
Apply modern data engineering practices while maintaining strong fundamentals in data modeling, ETL/ELT, orchestration, and data quality.
Responsibilities
- Design, develop, and maintain data pipelines, transformations, and orchestration workflows that ingest, curate, and publish data for analytics use cases.
- Build and enhance data models based on business analysis and reporting requirements.
- Work with structured and semi-structured data from enterprise systems and files, including relational databases, JSON, and XML.
- Collaborate with Data Analysts, source-system developers, and business stakeholders to define requirements and deliver fit-for-purpose data solutions.
- Improve engineering productivity by using AI tools for code support, testing framework creation, documentation, diagramming, and solution exploration.
- Support data quality, performance optimization, maintainability, and operational reliability of data solutions.
- Enable business reporting, self-service analytics, and foundational data assets for advanced analytics and data science teams.
- Follow established organizational standards for governance, security, access control, and data handling.
Education, Knowledge, Experience
- Minimum of 5-7 years’ hand-on experience.
- Background in programming, databases, data engineering, analytics engineering, or related technical disciplines; or degree in computer science, software engineering, information systems, economics, or other relevant engineering fields.
- Experience working with data warehousing, data marts, reporting environments, and cloud-based data platforms.
- Experience building ETL/ELT pipelines and supporting analytics-focused data solutions
Functional Competencies
- Knowledge of data and analytics frameworks supporting data lakes, warehouses, marts, and reporting.
- Strong understanding of data modeling principles and the ability to design solutions aligned with business objectives.
- Experience using AI for data engineering tasks such as design support, testing, documentation, structured prompting, and workflow improvement.
- Ability to work across the data lifecycle, from ingestion and transformation through delivery of trusted analytical data sets.
- Understanding of data quality, validation, monitoring, retention considerations, and operational support requirements.
- Ability to balance modern engineering practices with strong core data development fundamentals.
- Strong collaboration and communication skills when working with analysts, developers, and business stakeholders.
Technical Competencies
- Strong SQL and Python skills.
- Hands-on experience with Snowflake and ETL/ELT or data integration platforms; IICS experience is strongly preferred.
- Experience with data orchestration and workflow tools; familiarity with Airflow is a plus.
- Experience with data transformation and modeling tools such as dbt or equivalent frameworks.
- Familiarity with CI/CD practices, version control, and testing approaches for data solutions.
- Experience working with relational databases such as SQL Server and PostgreSQL.
- Ability to work with APIs and semi-structured data formats such as JSON and XML.
- Understanding of cloud and DevOps concepts is a plus.
- Ability to adapt to evolving tools, platforms, and AI-assisted engineering practices.
Skills Required
- Background in programming, databases and/or big data technologies OR BS/MS in software engineering, computer science, economics or related engineering field
- Knowledge of data and analytics frameworks supporting data lakes, warehouses, marts, and reporting
- Define data retention policies, monitor performance, and recommend infrastructure changes
- In-depth knowledge of data engineering discipline
- Extensive experience with Big Data tools and building data solutions for advanced analytics
- Minimum of 5+ years hands-on experience with a strong data background
- Solid programming skills in Java, Python and SQL
- Hands-on experience with database systems: Hadoop ecosystem, cloud platforms (AWS, Azure, Google Cloud), in-memory DBs (SAP HANA, Hazelcast), RDBMS (Teradata, SQL Server, Oracle), NoSQL (Cassandra, MongoDB, DynamoDB)
- Practical knowledge of data extraction and transformation tools (Informatica, Ab Initio, Alteryx) and modern big data ETL tools
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The Company
What We Do
Chubb is the world’s largest publicly traded property and casualty insurance company. With operations in 54 countries and territories, Chubb provides commercial and personal property and casualty insurance, personal accident and supplemental health insurance, reinsurance and life insurance to a diverse group of clients. As an underwriting company, we assess, assume and manage risk with insight and discipline. We service and pay our claims fairly and promptly. The company is also defined by its extensive product and service offerings, broad distribution capabilities, exceptional financial strength and local operations globally. Parent company Chubb Limited is listed on the New York Stock Exchange (NYSE: CB) and is a component of the S&P 500 index. Chubb maintains executive offices in Zurich, New York, London, Paris and other locations, and employs 31,000 people worldwide. Additional information can be found at: chubb.com.
