Lead Data Architect (Remote)

Coverys: Lead Data Architect The Lead Data Architect will lead the design, build, and evolution of our next-generation enterprise data platform & data integration pipeline. This role is central to our transformation toward a modern, governed, cloud-based data ecosystem that supports all functions at Coverys (notably underwriting, claims, actuarial, finance, and enterprise analytics etc.) The Lead Data Architect will define the architectural blueprint, establish data modeling standards, and lead the development of scalable, automated data pipelines in Snowflake. In addition to designing the architecture, this position will work closely with business and technology teams to ensure data is accurate, trusted, and available for advanced analytics, reporting, and operational decision-making. Essential Duties & Responsibilities Data Architecture Design & Modeling  Lead the design and maintenance of the enterprise data architecture aligned to business domains (Policy, Party, Claims, Billing, Underwriting, etc.).  Ensure data architecture design aligns to enterprise architecture standards & partners closely with Enterprise Architecture.  Develop canonical and semantic data models to support analytics, reporting, and operational use cases.  Define standards for data modeling, data quality, naming conventions, metadata, lineage, and documentation.  Partner with business stakeholders to translate requirements into scalable data structures and provide technical recommendations and tradeoffs. Data Engineering & Pipeline Development  Lead the design and development of ELT/ETL pipelines using modern cloud-native tools and frameworks.  Lead migration from legacy batch processes to automated, event-driven, or CDC-based ingestion patterns.  Implement data quality rules, validation frameworks, and reconciliation logic.  Optimize Snowflake workloads for performance, cost, and reliability.  Serve as a hands-on technical expert for the most complex data engineering challenges, including pipeline design, performance tuning, scalability, and production troubleshooting.  Design and build reusable data engineering frameworks, shared components, and reference implementations that improve development speed, consistency, and reliability.  Evaluate emerging data technologies and lead proofs of concept to determine practical adoption, integration, and enterprise scalability.  Define and apply engineering guardrails for security, observability, resiliency, recoverability, and operational readiness of critical data products.  Drive Snowflake workload optimization for performance, cost, and reliability.  Mentor the Lead of the Engineering team to ensure solution adheres to the right standards.  Ensure that the data engineering team is following sound technical processes and best practices. Cloud Data Platform Leadership  Design and oversee medallion-style data layers (bronze/silver/gold) for ingestion, curation, and consumption. Governance, Standards & Best Practices  Work with Data Governance to establish data dictionaries, lineage, classification, data quality and stewardship models and guide their implementation across data engineering solutions.  Ensure consistent use of canonical identifiers across systems and domains.  Promote data-as-a-product principles and guide teams toward reusable, scalable data assets. Collaboration & Leadership  Partner with business analysts, data scientists, actuaries, and analytics teams to support data needs.  Mentor SQL developers and analytics engineers transitioning into modern data engineering roles. Mentor and coach the data engineering team & lead the development of pipeline and architecture platforms. Provide technical direction, review designs and code, and help remove delivery blockers.  Provide architectural oversight and technical leadership for major data initiatives across the enterprise.  Support evolving business needs, as applicable. Education, Experience, Competencies & Values  Bachelor's degree in computer science or relevant field from an accredited college or university, required.  5-10 years of experience in data architecture and data engineering, including technical leadership experience, required.  Strong data modeling expertise (conceptual, logical, physical).  Hands-on experience designing enterprise data architecture.  Advanced ELT/ETL development experience (preferably cloud-native).  Deep experience with cloud data warehouses, ideally Snowflake.  Proficiency in Python for data engineering and automation.  Strong SQL skills and experience with large-scale data processing.  Experience with data quality frameworks, metadata management, and lineage.  Understanding of modern data patterns (CDC, event-driven ingestion, APIs, streaming, orchestration).  Experience in the insurance industry, preferred.  Snowflake certification, a plus.  Familiarity with tools such as dbt, Airflow, Azure Data Factory, or similar.  Knowledge of MDM, canonical modeling, and governance frameworks.  Experience with Power BI or other BI tools.  Mentoring data engineering teams.  Communicate designs clearly to the senior leadership team.  Qualified candidates must be eligible to work in the US without sponsorship or restriction. Specialization :  Machine Learning/Data Science