Lead Data Platform Architect

Job Responsibilities and Requirements

Serves as the recognized technical expert and architecture lead for a complex enterprise data platform and its connected ecosystem, spanning data lake, data warehouse, integration, semantic, and AI-ready capabilities. Defines the platform's target-state architecture and the reference designs, patterns, guardrails, and nonfunctional requirements that engineering teams build against, and personally designs and resolves the most complex and highest-risk platform architecture problems. Drives toward a unified, well-modeled view of business subject areas across operational and financial data, establishes architecture standards for data quality, lineage, and observability supporting analytics, machine learning, and generative AI, and evaluates emerging platform and AI capabilities. Remains engaged through implementation and stabilization by leading design and code-review standards for architectural alignment, resolving architecture deviations, and guiding Data Engineering and Platform Engineering & Operations partners, onsite and offshore teams, and vendors, while those teams retain ownership of pipeline delivery and day-to-day platform operations.

Key Responsibilities

• •Owns architecture direction across the major capabilities of the enterprise data platform, including ingestion, storage, distributed processing, data warehouse, integration, metadata, semantic layers, lineage, observability, security, and resilience.
• •Develops and maintains the platform target-state architecture and transition roadmap, driving toward a unified, consolidated view of business subject areas such as policies, claims, billing, rating, general ledger, and annuities.
• •Personally designs and resolves the most complex and highest-risk platform architecture problems, including cross-component data flows, platform consolidation, new data-asset ingestion, partner onboarding, and data conversions into new administrative systems.
• •Defines reusable reference designs, architecture patterns, guardrails, and nonfunctional requirements for scalability, performance, resilience, security, operability, and cost effectiveness that engineering teams apply.
• •Designs AI-ready data foundations and establishes architecture standards for data quality, lineage, and observability that keep model training and inference data accurate, reproducible, and auditable.
• •Leads data profiling and conceptual, logical, dimensional, and physical modeling for complex subject areas and sets modeling standards for the platform.
• •Evaluates emerging platform and AI capabilities, such as vector databases, semantic layers, and LLM-based tooling, through architecture assessment or proof of concept and recommends adoption approaches.
• •Leads technical design sessions and architecture reviews; reviews high-impact engineering designs and code for alignment with architecture standards and establishes design and coding practices that are maintainable and supportable.
• •Partners with Platform Engineering & Operations and Data Engineering on capacity, performance, batch run-time, and cloud-cost concerns by identifying architectural causes and defining architectural improvements and refactoring approaches.
• •Provides architecture leadership for severe or recurring incidents and major defects with architectural causes, including root-cause analysis and remediation plans, and coordinates resolution with engineering and operations teams.
• •Partners with data governance, security, compliance, and audit teams to ensure platform architecture supports enterprise governance frameworks, security standards, AI-related data use, and financial-reporting controls such as SOX.
• •Serves as architecture point of contact for business stakeholders and IT partners, including application owners, integration teams, and vendors, on platform capabilities and data-exchange interfaces.
• •Mentors architects and engineers, provides input into release planning and change-request scoping, and influences platform roadmaps, sequencing, and technical investment decisions.

For the initial 6–12 months, this position will have expanded accountability for the implementation and operational stability of the enterprise data platform. The Lead Data Platform Architect is accountable for the outcomes below, while Data Engineering and Platform Engineering & Operations resources perform much of the hands-on development and operational work.

• •Provides technical direction for the design, build, scaling, and refactoring of data pipelines and platform solutions, including developing pseudocode, templates, reference implementations, and reusable patterns for engineers to follow.
• •Is accountable for Data Lake and Data Warehouse stability and achievement of established batch-processing service levels, working through Data Engineering and Platform Engineering & Operations teams to address performance and reliability concerns.
• •Leads response to significant incidents and recurring platform issues, including root-cause analysis, remediation planning, prioritization, and coordination of the technical resources performing the work.
• •Oversees platform capacity, utilization, performance, and cloud-cost concerns and establishes technical direction for required architecture, engineering, or operational improvements.
• •Provides accountability and technical oversight for platform releases, data quality controls, and SOX-related controls, partnering with first-line managers and applicable governance, release, compliance, engineering, and operations resources.
• •Provides technical leadership to onsite and offshore engineering and platform operations resources, including establishing standards, reviewing deliverables, resolving technical barriers, and confirming alignment with the approved architecture.
• •Supports the transition of operational accountabilities to first-line Data Engineering and Platform Engineering & Operations managers as the platform support model matures, while retaining accountability for architecture standards, design integrity, and significant architecture decisions.
• •Other duties as assigned.

Required Qualifications

or equivalent experience gained from any combination of formal education, on-the-job training, and/or work and life experience

• •Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, Mathematics, or a related field, or equivalent experience gained through education, training, certifications, and work experience.
• •Typically 8-11 years of progressive relevant experience in data engineering, data architecture, or data platforms, including technical leadership of complex enterprise data platform initiatives and onsite and offshore technical teams.
• •Expert knowledge of enterprise data platform architecture across data lake, data warehouse, and cloud data platforms, including object storage, managed big data services, and distributed processing (e.g., Spark-based platforms, Snowflake, Redshift, Teradata, or equivalent).
• •Expert data modeling skills in relational and big data environments, including dimensional modeling (Kimball methodology, star schemas, bridge tables) and data modeling tools.
• •Strong hands-on proficiency with SQL and working knowledge of Spark, Spark SQL, Scala, Python, PL/SQL, Linux and shell scripting, ETL/ELT and data-integration tools, streaming platforms, and job scheduling sufficient to direct and review implementation designs and code.
• •Deep knowledge of data quality, lineage, observability, security, and governance practices, and of production support of enterprise batch environments, incident management, and root-cause analysis.
• •Experience with performance tuning, capacity planning, and cloud cost optimization as they relate to platform architecture.
• •Knowledge of AI-ready data architecture, including semantic layers, vector databases or vector search, LLM-based tooling, and preparing and governing data for machine learning training and inf