Data Engineer III

FM is a leading property insurer of the world's largest businesses, providing more than one-third of FORTUNE 1000-size companies with engineering-based risk management and property insurance solutions. FM helps clients maintain continuity in their business operations by drawing upon state-of-the-art loss-prevention engineering and research; risk management skills and support services; tailored risk transfer capabilities; and superior financial strength. To do so, we rely on a dynamic, culturally diverse group of employees, working in more than 100 countries, in a variety of challenging roles. The Data Engineer III is responsible for analysis, data modeling, data collection, data integration, and preparation of data for consumption. The Data Engineer is responsible for creating and managing data infrastructure, data pipeline design, implementation and data verification. Along with the team, the Data Engineer is responsible for ensuring the highest standards of data quality, security and compliance. Additionally, the Data Engineer will implement methods to improve data reliability and quality, combine raw information from different sources to create consistent data sets. The Data Engineer will need to be proficient in DataOps and be able to provide technical expertise to other Data Engineers. Data Engineers will design and build data solutions and integrations which may involve diverse data platforms, software, hardware, technologies and tools. Uses available approved technologies and recommends solution options. Incumbents may design and build application solutions and integrations which may involve diverse development platforms (including 3rd party systems), software, hardware, technologies and tools. The Data Engineer III is the third level position in the Data Engineer job family. Those holding this position are typically assigned to work on integrated project teams for medium to large projects and be the lead for smaller projects. The Data Engineer III must also be able to work independently. Data Acquisition: - Possess and continually grow knowledge of structured and unstructured data sources within each product journey (Underwriting and Risk; Client Service, Sales and Marketing; Claims; Account and Location Engineering) as well as emerging data sources (purchased data sets; external data; etc.) - Partner with product owners, developers, solution architects, business analysts, data engineers, data analysts, data scientists and others to understand data and reporting needs - Develop solutions using data modeling techniques and using cloud technologies such as Postgres, Azure Data Factory, streaming technologies such as Confluent/Kafka, technologies such as Synapse, SQL Server, SSIS and others as required - Validate solutions are accurate through detailed and disciplined testing methodologies - Ensure tables and views are designed for data integrity, efficiency and performance, and are easy to comprehend. Move and Store Data: - Data flow, infrastructure pipelines, ETL/ELT, structured and unstructured data movement and storage solutions - Design data models and data flows into and out of Data Analytics databases - Understand and design data relationships between business and data subject ar