Raleigh)

Arch Capital Group•Jersey City, NJ

1d•Hybrid

About The Position

The Assistant Vice President of Enterprise Reporting and Data leads enterprise reporting and data strategy across critical functions, including data engineering, data governance, agentic AI, business intelligence and reporting, actuarial data support, and specialized areas such as Ceded Reinsurance. This role owns end-to-end data and reporting platforms, ensuring scalable architecture, efficient operations, and business-aligned delivery across agile teams and vendor partners. The AVP also drives cross-functional collaboration, business value, compliance, and modernization across the data ecosystem, including Snowflake, Azure services, Databricks, Sigma, Python, Airflow, Collibra, Stibo, and Power BI. In addition, the role is responsible for leading delivery, governance, and operational oversight across key enterprise data platform capabilities, including Data Stores, Cortex, Data Security, MDM, RDM, Data Lineage, and Data Catalog. The AVP will provide leadership across Agile execution and solution architecture while ensuring strong platform design governance, scalability, and standardization. A core expectation is maintaining close proximity to delivery, actively guiding engineering teams and course-correcting execution when needed. The role partners directly with SVP-level stakeholders to drive requirement alignment, negotiation, and business outcomes rather than operating as an order-taker. It also plays a key role in advancing AI-enabled reporting and modernization efforts across the AINA data ecosystem while ensuring operational stability and compliance.

Requirements

12+ years of experience in data platforms, data engineering, and business intelligence.
7+ years of leadership experience managing multidisciplinary teams across data engineering, reporting, actuarial data, or reinsurance.
Strong experience in insurance (P&C), finance, actuarial, or risk domains is highly preferred.
Deep knowledge of cloud technologies, especially Azure and Snowflake, with familiarity in Power BI, SSIS, and SSRS.
Must demonstrate strong proximity to delivery (not just oversight), including ability to review code, guide engineering teams on “how to build,” and step in to course-correct technical execution when needed.
Proven experience engaging directly with SVP-level stakeholders to drive and negotiate requirements, with accountability for outcomes rather than operating as a downstream delivery partner.
Working, hands-on proficiency in Python within data engineering contexts, with the ability to apply it in pipelines or guide teams in its practical use (not just theoretical exposure)
Experience with regulatory compliance and audit standards, including SOX, SOC 2, and NYDFS.
Proven success leading large-scale data transformation programs and cross-functional delivery teams.
Ability to mentor senior managers, architects, and subject matter experts, with strong stakeholder management and influencing skills.
Demonstrated experience building data capabilities and tools to support analytics, research, and actuarial functions in an insurance environment.
Strong proficiency in SQL, Python, or similar programming languages.
Experience with orchestration tools such as Airflow.
Experience with cloud platforms and tools such as Snowflake and Databricks.
Ability to work independently while managing priorities and collaborating across teams.
Excellent collaboration and relationship-building skills.
Comfort with ambiguity and the ability to break complex problems into manageable steps.
Strong analytical skills and the ability to derive insights from data.
Strong problem-solving and critical-thinking skills.
Excellent verbal and written communication skills, with the ability to tailor messages for technical and non-technical audiences, including senior executives.
Flexibility to adapt to changing priorities and requirements.
Ability to lead and mentor mid-level and junior team members in delivering high-quality solutions to complex business challenges.
Curiosity and creativity in exploring modern technologies and new data sources.
Ability to document data sources and technical solutions effectively.
Ability to balance competing priorities across portfolios and resources.
Ability to build strong partnerships across the organization to support strategic goals.
Commitment to continuously improving team capabilities and workflows.

Nice To Haves

Strong experience in insurance (P&C), finance, actuarial, or risk domains is highly preferred.
Familiarity in Power BI, SSIS, and SSRS.

Responsibilities

Set the vision and strategy for data initiatives across key product teams, including Enterprise Reporting, Ceded Reinsurance, and Actuarial IT.
Partner with senior business stakeholders to align data initiatives with enterprise goals.
Lead cross-functional agile teams to deliver high-quality data products and platforms that create business value.
Lead the roadmap, architecture, and lifecycle management of enterprise data platforms, including Snowflake-based data lakes, data warehouses, and Azure pipelines.
Ensure reliable development, deployment, and support of ETL/ELT frameworks using tools such as Snowflake, DBT, Databricks, Python, Qlik, Airflow, and ADF.
Lead strategy and development for Sigma, Power BI, SSRS, and dashboarding solutions.
Partner with business teams to deliver actionable insights, self-service reporting, and financial and statutory reporting support.
Provide strategic and technical leadership for teams supporting enterprise reporting and data, actuarial modeling, reserving data flows, and ceded reinsurance data provisioning.
Ensure timely, accurate, and audit-ready data for reporting platforms, actuarial processes, and reinsurance calculations and reporting.
Lead multiple delivery teams across the U.S. and offshore locations, maintaining sprint velocity and roadmap execution.
Ensure delivery complies with SOX, SOC 2, and NYDFS requirements.
Manage vendor relationships, contract negotiations, and budget planning.
Promote modern agentic AI, DevOps, CI/CD, Git, and orchestration practices across data engineering and reporting pipelines.
Drive innovation in AI/ML, data observability, and self-service capabilities to improve business outcomes.
Ensure strong documentation, test coverage, and audit trails across data processes and platforms.