Principal Data Architect
Key Skills for This Role
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Qualifications
- Significant experience in data engineering, database architecture, analytics engineering, or software engineering, including recent ownership of data architecture in AWS environments.
- A strong record of designing enterprise data platforms and data-intensive products that serve both operational and analytical workloads.
- Deep expertise in data modeling, including relational, dimensional, domain-driven, event-based, document, key-value, and other NoSQL approaches.
- Strong SQL and database engineering knowledge across technologies such as PostgreSQL, SQL Server, MySQL, Aurora, Redshift, and DynamoDB.
- Practical understanding of data lake, lakehouse, warehouse, streaming, event-driven, and API-based architecture patterns on AWS.
- Experience designing data pipelines and integrations using services such as AWS Glue, Kinesis, Lambda, Step Functions, SQS, SNS, and APIs.
- Strong knowledge of data governance, metadata, lineage, quality, privacy, security, retention, access control, and the operational ownership of data products.
- Experience using infrastructure as code and CI/CD practices to make data platforms repeatable, controlled, testable, and supportable.
- Ability to communicate complex data concepts, architecture choices, dependencies, and risks to technical teams, product leaders, and business stakeholders.
- Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, Mathematics, or a related discipline, or equivalent professional experience.
- #LI-MB1
- #LI-Hybrid
Responsibilities
- Define the target data architecture and phased roadmap for operational systems, data lakes, warehouses, integration services, analytics platforms, and customer-facing data products.
- Establish domain-oriented data models and product boundaries that clarify ownership, reduce duplication, and support consistent use of core business data.
- Design logical and physical models, canonical schemas, event structures, data contracts, and master and reference data approaches across relational, dimensional, and NoSQL environments.
- Set architecture standards for data ingestion, transformation, storage, sharing, retention, archiving, and deletion across batch, streaming, API, and event-driven patterns.
- Architect AWS data solutions using appropriate services such as S3, Lake Formation, Glue, Redshift, Aurora, RDS, DynamoDB, Kinesis, Lambda, Step Functions, SQS, SNS, and API Gateway.
- Connect transactional applications with reporting, business intelligence, advanced analytics, machine learning, and external customer use cases without compromising operational integrity.
- Define expectations for metadata, cataloging, lineage, provenance, quality, observability, semantic consistency, and service levels so data can be understood and trusted.
- Embed privacy and security into data design, including IAM, encryption, masking, tokenization, tenant isolation, policy-based access, auditability, retention, and permitted-use controls.
- Guide database and workload design across relational, NoSQL, warehouse, and object storage technologies, including partitioning, indexing, query patterns, scalability, reliability, and cost.
- Partner with product and business teams to translate information needs into data capabilities, making dependencies, constraints, and trade-offs explicit.
- Evaluate new AWS services, data patterns, and platform capabilities through focused proofs of concept and evidence-based recommendations.
- Provide architecture oversight from discovery through production and coach data engineers, software engineers, analysts, and technical leads in effective data design
About Verisk Analytics
Global data analytics and technology partner to the insurance industry.
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