Senior Data Software Engineer, Tech Lead - NYC

The Senior Data Software Engineer, Tech Lead position in the Data Strategy team presents a chance to create and execute data products for the world's biggest and most reputable reinsurance brokerage. Data Strategy has a “start-up style” mandate (within a $2 billion company) to enhance the acquisition, storage, analysis, fidelity, and monetization of client, internal, and third-party data across the organization. This innovation spans our petabyte-scale insured assets, including property, business, marine, and aviation entities, and their associated risks, such as hurricanes, wildfires, cyber-attacks, and wars, in a financial and economic context.

As a member of the Data Strategy group, the Senior Software Engineer will work with fellow data and web engineers, 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 the organization and (2) driving the monetization of data via newly designed and existing products for the organization's reinsurance clients. The Senior Software Engineer will be the head facilitator on multiple innovative initiatives and will have ownership over the design, development, and delivery of projects requiring direct reporting to senior-level management in both business and technical groups.

Leadership Responsibilities

1. Work with a product manager as technical lead of a team of ~5 engineers, data scientists, and analysts to design, scope, and oversee work in an Agile environment.
2. Manage junior data and web engineers, focusing on productivity, quality, and professional development.
3. Partner with the head of Data Strategy and other senior engineers to create and evangelize best-in-class engineering competency and tooling within the organization.
4. Enforce strong development standards across the team through code reviews, automated testing, and monitoring.
5. Establish strong relationships with internal clients as an engineering representative for data strategy.
6. Contribute to the overall Data Strategy vision and execution via quarterly planning and executive committee reporting.
7. Partner regularly improving engineering recruiting process for the required skillsets and resourcing demands.
8. Learn the complex business of reinsurance to coach data technologists and execute the team's initiatives more effectively.

Software Engineer Responsibilities

1. Develop, implement, and deploy custom data pipelines powering machine learning algorithms, insights generation, client benchmarking tools, business intelligence dashboards, reporting, and new data products.
2. Innovate new ways to leverage large and small datasets to drive revenue via the development of new products with the Data Strategy team, as well as the enhancement of existing products.
3. Architect engineering solutions using the latest cloud technologies in a process that spans hypothesis-validating prototypes to large-scale production data products, ensuring internal security and regulatory compliance.
4. Design solutions that account for unstructured data and document management system(s), including ingesting, tracking, parsing, analyzing, and summarizing documents at scale.
5. Perform exploratory and goal-oriented data analyses to understand and validate the requirements of data products and help create product roadmaps.
6. Develop, implement, and deploy front-ends and APIs, which may involve business intelligence dashboards, data pipelines, machine learning algorithms, and file ingestion mechanisms.
7. Work closely with data scientists, data engineers, web engineers, PMs, and other stakeholders to design & develop products.
8. Keep current on the latest trends and innovations in data technology and how these trends apply to the organization's business and data strategy.

Required Qualifications

1. 5-8+ years of relevant experience in data-focused software engineering.
2. Master’s Degree or Ph.D. in data science, computer science, or related quantitative field such as applied mathematics, statistics, engineering, or operations research, or equivalent experience.
3. Experience in Python and familiarity with OOP and functional programming principles.
4. Strong knowledge of SQL and familiarity with the high-level properties of modern data stores.
5. Strong understanding of the contemporary SDLC, including dev/QC/prod environments, unit/integration/UA testing, CI/CD, etc.
6. Experience building and maintaining CI/CD pipelines with tools such as Azure DevOps, GitLab, Travis, Jenkins, etc.
7. At least two and ideally all of the following sets of experience:
1. Data Engineering
   • 2+ years’ experience with data engineering.
   • Extensive experience with (py)Spark, Python, JSON, and SQL.
   • Experience integrating data from semi-structured and unstructured sources.
   • Knowledge of various industry-leading SQL and NoSQL database systems.
2. Backend Web
   • 2+ years of backend/full-stack web engineering.
   • Experience working with Python-based server-side web frameworks like FastAPI or Django.
   • Experience with complex backends involving multiple data stores, asynchronous worker queues, pub-sub messaging, and the like.
   • Knowledge of cloud-based web deployments (AWS/Azure/GCP, Kubernetes, auto-scaling, etc.).
   • Experience with one or more major frontend frameworks (React strongly preferred).
3. Data Science/Analytics
   • 2+ years of data analysis, AI, or data science work.
   • Experience with data cleaning, enrichment, and reporting to business users.
   • Experience selecting, training, validating, and deploying machine-learning models.
   • Experience with or strong interest in learning about LLMs in a productized context.
8. Experience working in an Agile environment to facilitate the quick and effective fulfillment of group goals.
9. Good interpersonal and communication skills for establishing and maintaining sound internal relationships, working well as part of a team, and for presentations and discussions.
10. Strong analytical skills and intellectual curiosity (interest in the meaning and usefulness of the data), as demonstrated through academic experience or work assignments.
11. Excellent English verbal and writing skills for complex communications with your colleagues in all departments and levels of the organization, including communicating technical concepts to a non-technical audience.
12. Good ability to prioritize workload according to volume, urgency, etc., and to deliver on required projects in a timely fashion.

Preferred Qualifications

1. Strong understanding of entity resolution, streaming technologies, and ELT/ETL frameworks.
2. Experience with web scraping and crowdsourcing technologies.
3. Experience with Databricks and optimizing Spark clusters.
4. Experience architecting web ecosystems from the ground up, including monolith vs. microservice decisions, caching technologies, security integrations, etc.
5. Experience working with data visualization dashboarding tools (PowerBI, Tableau).
6. Insurance domain knowledge or strong interest in developing it.
7. Experience with the MS Azure cloud environment.