Manager, Data Engineer (Remote)

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.

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.

Desired Skills

• Data Engineering: 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).
• Analytics & AI Readiness: Insurance data modeling for 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.
• Leverage existing tools and technologies to build and maintain data pipelines.

Required Skills

• 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.

Education

College degree in Computer Science, Engineering, Statistics, Mathematics, Actuarial Science, Data Analytics, or equivalent.

Benefits

• Multiple medical plans plus dental, vision and prescription drug coverage.
• A competitive 401k with generous matching.
• Paid parental leave of up to 10 weeks.
• Student loan assistance and tuition reimbursement.
• Backup child and elder care.
• Volunteer time off.
• Life and AD&D insurance.
• Short and long-term disability.
• Comprehensive benefits package.