Principal Data Architect

Overview Position Summary: Mercury Insurance is seeking a Principal Data Architect to lead the strategy, design, and evolution of our enterprise data ecosystem. This leader will partner closely with Engineering, Data Science, and business teams to define and execute a scalable data architecture that supports analytics, operational reporting, and data-driven product capabilities. This is a hands-on technical leadership role that blends deep data architecture expertise, architectural thinking, and cross-functional influence. The Principal Data Architect will own the long-term direction for enterprise data models, pipelines, and platform standards while guiding teams responsible for delivering reliable, governed, and high-performing data solutions. This role is accountable for building a modern data foundation that is scalable, secure, and aligned to real business needs across Mercury. Geo-Salary Information An in-person interview may be required during the hiring process State specific pay scales for this role are as follows: $107,344.71 - $300,603.92 (NJ, NY, WA, HI, AK, MD, CT, RI, MA) $107,344.71 - $300,603.92 (NV, OR, AZ, CO, WY, TX, ND, MN, MO, IL, WI, FL, GA, MI, OH, VA, PA, DE, VT, NH, ME) $107,344.71 - $300,603.92 (UT, ID, MT, NM, SD, NE, KS, OK, IA, AR, LA, MS, AL, TN, KY, IN, SC, NC, WV) In CA: Typical hiring range is $201,933.00 - $280,462.00 The expected base salary for this position will vary depending on a number of factors, including relevant experience, skills and location. Responsibilities Essential Job Functions:  Enterprise data strategy and architecture Define and lead the enterprise data architecture strategy, target state, and multi-year roadmap for Mercury’s data platform Establish reference architectures, standards, and guardrails for data ingestion, transformation, modeling, orchestration, quality, observability, and consumption Drive architecture decisions for enterprise data platforms, including EDW, lakehouse, streaming, operational data integration, and domain-oriented data products Partner with senior Technology and business leaders to align data investments to enterprise priorities, business value, and long-term scalability Evaluate current-state architecture, identify gaps, and lead rationalization of tools, patterns, and technical debt across the data ecosystem Technical leadership and architecture enablement Provide technical direction and architectural leadership to data engineering, analytics engineering, and platform teams Set standards for design quality, model integrity, operational excellence, and scalable delivery across the Enterprise Data & Operations function Mentor engineers and technical leaders in architectural thinking, modern engineering practices, and delivery excellence Build an automation-first culture focused on reliability, repeatability, maintainability, and continuous improvement Raise the bar on technical quality, design rigor, and execution across the data engineering organization Platform and solution delivery Design, develop, and oversee end-to-end enterprise data solutions supporting multiple data domains, data marts, and analytics use cases Guide the design and modernization of foundational enterprise data models, including decisions around grain, entities, relationships, conformed dimensions, and slowly changing dimensions Ensure scalable batch and streaming data pipelines are built to support both enterprise reporting and advanced analytics environments Drive implementation of layered data architecture patterns, including Bronze/Silver/Gold or equivalent logical data zones Partner with Engineering teams to productionize data pipelines with strong performance, resiliency, and operational supportability Data reliability, governance, and operational excellence Own the reliability, quality, consistency, and observability of Mercury’s core data assets and pipelines Establish and enforce data quality frameworks, automated testing, lineage, monitoring, alerting, and recovery processes Define service levels and operational standards for critical data products and pipelines Reduce manual processes and technical debt through standardization, automation, and disciplined platform engineering Partner with security, compliance, and governance stakeholders to ensure data architecture aligns with enterprise risk and control requirements Cross-functional influence and innovation Translate business problems into scalable data products, architecture patterns, and prioritized roadmaps Partner across Product, Engineering, Data Science, Analytics, and business teams to ensure the data platform enables real business outcomes Lead proof of concepts, architecture reviews, and technology evaluations for new tools and capabilities Influence vendor selection, platform direction, and engineering standards through fact-based analysis and practical technical leadership Identify opportunities to apply GenAI and LLM capabilities to improve engineering productivity, data operations, governance, and insight generation Qualifications Education: Bachelor’s degree in computer science, Engineering, Information Systems, or a related field; Master’s degree preferred Experience: 12+ years of experience in data engineering, data architecture, or enterprise data platform leadership 5-10 years of experience leading, mentoring, and growing high-performing data engineering or analytics engineering teams Knowledge and Skills: Proven experience defining enterprise data strategy and leading large-scale modernization of data pipelines, platforms, and models Deep expertise in enterprise data modeling, including 3NF, dimensional, star, and snowflake patterns, with strong judgment on how to model real-world business processes Strong experience redesigning foundational data models and pipelines with a focus on scalability, usability, and reliability Expert-level SQL and Python