Principal Data Architect

Role Description 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. Responsibilities 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 Bachelor’s degree in computer science, Engineering, Information Systems, or a related field; Master’s degree preferred. 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. 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 skills, with strong production experience in Informatica and dbt, including models, testing, and package management. Experience with orchestration frameworks such as Airflow, Dagster, Tivoli, or similar tools. Familiarity with streaming and event-driven data technologies such as Kafka or comparable platforms. Hands-on experience with modern warehouse and lakehouse platforms such as Snowflake, Databricks, Redshift, or BigQuery. Strong understanding of cloud-native engineering practices across AWS, GCP, or Azure. Demonstrated commitment to engineering best practices, including Git, CI/CD, infrastructure automation, testing, and DRY design principles. Experience implementing data quality, observability, lineage, and operational controls in production environments. Strong stakeholder management and communication skills, with the ability to influence technical and non-technical leaders. Data product mindset with the ability to turn business needs into architecture, roadmaps, and execution plans. Experience in insurance, SaaS, or marketplace environments is a plus. Experience leveraging GenAI or LLM platforms such as OpenAI, Claude, or Gemini to solve meaningful business and engineering problems is strongly preferred. Benefits Competitive compensation. Flexibility to work from anywhere in the United States for most positions. Paid time off (vacation time, sick time, 9 paid Company holidays, volunteer hours). Incentive bonus programs (potential for holiday bonus, referral bonus, and performance-based bonus). Medical, dental, vision, life, and pet insurance. 401 (k) retirement savings plan with company match. Engaging work environment. Promotional opportunities. Education assistance. Professional and personal development opportunities. Company recognition program. Health and wellbeing resources, including free mental wellbeing therapy/coaching sessions, child and eldercare resources, and more.