Manager, Data Engineer (Remote)

Arch Capital Group•Raleigh, NC

16h•Remote

About The Position

Strategic Analytics at Arch is a growing team at the forefront of the company’s AI transformation. We design and deploy agentic AI systems and predictive analytics, supported by AI-ready data assets and AI-assisted development practices. These capabilities are becoming increasingly embedded across the enterprise. Data is central to our mission. We unify internal and external data on modern cloud platforms—including Snowflake and Databricks within the Azure ecosystem—to produce reliable, analytics-ready data assets that support both traditional analytics and emerging AI use cases. As Manager of Strategic Analytics Services, supporting the Claims Analytics group, you will lead end-to-end delivery of complex data pipelines that put analytics at the center of business processes. This is a hands-on role that combines execution, technical leadership, and stakeholder partnership, including leading and developing a team of data engineers. You will work closely with business and technical leaders to align priorities, shape scalable data solutions, and deliver measurable outcomes. You will also guide engineers and reinforce strong delivery practices, while advancing the team’s capabilities in modern data engineering, AI-assisted development, and well-governed, reusable data systems.

Requirements

Strong programming expertise in Python and SQL, including data engineering frameworks, large-scale data manipulation, and governed AI-assisted development practices
Apache Spark proficiency ( PySpark preferred) and experience with distributed data processing, including building scalable pipelines and optimizing performance for large-scale datasets
Cloud data platform proficiency (Snowflake, Databricks, Azure ecosystem fundamentals)
Data warehousing and modeling fundamentals (schema design, conformed definitions, performance optimization)
Data quality and observability practices (testing, reconciliation, monitoring)
Insurance data modeling f or analytics and actuarial-ready data structures
MLOps familiarity supporting operationalized analytics and models
Semantic modeling skills (business definitions, metrics, ontology/taxonomy/domain models)
Business definition standardization for reuse across BI and AI use cases
Self-directed execution and ownership in a distributed environment, combined with strong cross-functional collaboration, stakeholder partnership, and team building
Strong problem-solving and critical thinking skills, including the ability to decompose complex challenges
Clear communication across technical and business audiences, with the ability to adapt effectively in ambiguous and evolving environments
6+ years’ experience of hands-on development in Python and distributed processing environments (e.g., Spark)
2–3+ years of technical leadership or project delivery ownership experience
Hands-on Databricks experience highly preferred
College degree in Computer Science, Engineering, Statistics, Mathematics, Actuarial Science, Data Analytics, or equivalent.

Responsibilities

Lead delivery of high-quality data solutions by partnering with stakeholders and coaching data engineers.
Own end-to-end data engineering delivery across the project lifecycle.
Build strong partnerships across the organization to align priorities and deliver data-related goals.
Design clear, analytics-ready data structures by anticipating downstream analytical needs.
Evaluate and adopt new technologies and data sources to improve capability and efficiency.
Automate data ingestion and integration to reliably connect internal and external data sources.
Document data sources, definitions, and technical solutions to support transparency and reuse.
Reinforce strong delivery hygiene (version control, code review, automated testing, CI/CD, and operational readiness/monitoring).
Apply agentic, AI-assisted coding practices to accelerate delivery while maintaining appropriate controls .
Build agent-ready data assets, including semantic layer components (ontology, taxonomy, domain models) and governed access.
Provide retrieval-ready context (RAG pipelines, vector stores, knowledge bases) when needed.