Data Engineer

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Job description

Role

The Data Engineer role within the Data Strategy group at Guy Carpenter ("GC") provides an opportunity to own the build-out and implementation of data solutions to power one of the world's largest and most respected risk management and reinsurance firms. The Data Strategy group has a "start-up style" mandate and internal consulting role (within a $1.3 billion company) to effectively enhance the acquisition, storage, analysis, fidelity, and monetization of massive amounts of client, internal, and third-party data across the GC organization.

As a member of the Data Strategy group, the Data Engineer will work with fellow data scientists, product managers, business analysts, and stakeholders from other internal groups to design and improve data-centric projects with the dual mandate of (1) increasing the efficiency of the data collection and analysis process across GC and (2) driving the monetization of data via newly designed and existing products for GC's reinsurance clients. The data engineer will be the lead facilitator on innovative initiatives and will have ownership over the design, development, and delivery of projects which will require direct reporting to senior-level management in both business and technical groups.

Responsibilities

• Develop, implement, and deploy custom data pipelines powering machine learning algorithms, insights generation, client benchmarking tools, business intelligence dashboards, reporting and new data products.
• Innovate new ways to leverage enormous amounts of various datasets to drive revenues via the development of new products with the Data Strategy team, as well as the enhanced delivery of existing products
• Consume data from a variety of sources (relational DBs, APIs, NetApp and other cloud storage, FTPs) & formats (excel, CSV, XML, parquet, unstructured))
• Construct and maintain data pipelines between GC's databases, and other sources, with the data lake utilizing modern ETL frameworks
• Own the role of data steward for a variety of high value datasets and implement innovative quality assurance practices
• Establish and implement metadata management standards and capabilities, including lineage mapping
• Enforce strong development standards across the team through code reviews, unit testing, and monitoring
• Perform basic data analysis within Jupyter Notebooks to validate the fulfillment of requirements for data pipelines
• Evangelize data strategy techniques and best practices throughout global strategic advisory
• Keep up-to-date on the latest trends and innovation in data technology and how these trends apply to GC's business and data strategy

Required Qualifications

• 3-5 years of relevant experience as a data engineer or in a similar role
• Bachelor's or master's degree in data science, computer science or related quantitative field such as applied mathematics, statistics, engineering, or operations research
• Extensive experience with Spark, Python, and SQL
• Extensive experience integrating data from semi-structured sources
• Experience deploying/maintaining cloud resources (AWS, Azure, or GCP)
• Knowledge of various industry-leading SQL and NoSQL database systems
• Experience working in an Agile environment to facilitate the quick and effective fulfillment of group goals
• Good interpersonal skills for establishing and maintaining good internal relationships, working well as part of a team and for presentations and discussions
• Strong analytical skills and intellectual curiosity as demonstrated through academic experience or work assignments
• Good ability to prioritize workload according to volume, urgency, etc. and to deliver on required projects in a timely fashion

Preferred Qualifications

• Strong understanding of entity resolution, streaming technologies, and ELT/ETL frameworks
• Ability to articulate the advantages of various cloud and on-premises deployment options
• Experience with Master Data Management
• Experience with web scraping and crowd sourcing technologies
• Familiarity with modern data productivity frameworks and their alternatives such as Databricks, DataRobot, and Alteryx
• Experience with the MS Azure cloud environment, including ARM template deployments
• Strong knowledge of CI/CD principles and practical experience with a CI/CD technology (Azure Devops, GitLab, Travis, Jenkins)