Manager, Data Engineer
With a company culture rooted in collaboration, expertise and innovation, we aim to promote progress and inspire our clients, employees, investors and communities to achieve their greatest potential. Our work is the catalyst that helps others achieve their goals. In short, We Enable Possibility℠.
Job Summary
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.
Responsibilities
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Lead delivery of high-quality data solutions by partnering with stakeholders and coachingdataengineers.
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Own end-to-end data engineering delivery across the project lifecycle.
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Build strong partnerships across the organization to align priorities anddeliverdata-related goals.
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Design clear, analytics-ready data structures byanticipatingdownstream analytical needs.
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Evaluate and adoptnew technologiesand data sources to improve capability and efficiency.
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Automate data ingestion and integration to reliably connect internal and external data sources.
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Document data sources, definitions, and technical solutions to support transparency and reuse.
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Reinforce strong delivery hygiene (version control, code review, automated testing, CI/CD, and operational readiness/monitoring).
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Apply agentic, AI-assisted coding practices to accelerate delivery whilemaintainingappropriate controls.
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Build agent-ready data assets, including semantic layer components (ontology, taxonomy, domain models) and governed access.
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Provide retrieval-ready context (RAG pipelines, vector stores, knowledge bases) when needed.
Desired Skills
Data Engineering
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Strong programmingexpertisein Python and SQL, including data engineering frameworks, large-scale data manipulation, and governed AI-assisted development practices
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Apache Sparkproficiency(PySparkpreferred) and experience with distributed data processing, including building scalable pipelines andoptimizingperformance for large-scale datasets
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Cloud data platformproficiency(Snowflake, Databricks, Azure ecosystem fundamentals)
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Data warehousing and modeling fundamentals (schema design, conformed definitions, performance optimization)
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Data quality and observability practices (testing, reconciliation, monitoring)
Analytics & AI Readiness
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Insurance data modelingforanalytics and actuarial-ready data structures
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MLOpsfamiliarity supporting operationalized analytics and models
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Semantic modeling skills (business definitions, metrics, ontology/taxonomy/domain models)
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Business definition standardization for reuse across BI and AI use cases
Leadership &Operating Model
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Self-directed execution and ownership in a distributed environment, combined with strong cross-functional collaboration, stakeholder partnership, and team building