Principal Data Architect

Salary: $121,000 - 161,000 per year Requirements:

• Significant experience in data engineering, database architecture, analytics engineering, or software engineering, including recent ownership of data architecture in AWS environments.
• Demonstrated success designing enterprise data platforms and data-intensive products for operational and analytical workloads.
• Deep knowledge of relational, dimensional, domain-driven, event-based, document, key-value, and other NoSQL data modeling approaches.
• Strong SQL and database engineering skills across technologies such as PostgreSQL, SQL Server, MySQL, Aurora, Redshift, and DynamoDB.
• Practical understanding of AWS data lake, lakehouse, warehouse, streaming, event-driven, and API-based architecture patterns.
• Experience building data pipelines and integrations with services such as AWS Glue, Kinesis, Lambda, Step Functions, SQS, SNS, and APIs.
• Strong understanding of data governance, metadata, lineage, quality, privacy, security, retention, access control, and operational ownership of data products.
• Experience with infrastructure as code and CI/CD practices for repeatable, controlled, testable, and supportable data platforms.
• Ability to explain complex data concepts, architecture decisions, dependencies, and risks to technical teams, product leaders, and business stakeholders.
• Bachelors degree in Computer Science, Data Engineering, Software Engineering, Mathematics, or a related discipline, or equivalent professional experience.

• Define our target data architecture and phased roadmap across operational systems, data lakes, warehouses, integration services, analytics platforms, and customer-facing products.
• Establish domain-oriented data models and product boundaries that clarify ownership, reduce duplication, and promote 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 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, including 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 while protecting operational integrity.
• Define expectations for metadata, cataloging, lineage, provenance, quality, observability, semantic consistency, and service levels so our data is understandable and trusted.
• Build privacy and security into data design, covering 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.
• Work with product and business teams to translate information needs into data capabilities and make dependencies, constraints, and trade-offs clear.
• Assess 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.

• API
• AWS
• AWS Glue
• Lambda
• Redshift
• Architect
• Business Intelligence
• CI/CD
• IAM
• Machine Learning
• MySQL
• NoSQL
• PostgreSQL
• SQL
• Security
• Cloud
• Support

More:

We are Verisk, and hackajob is partnering with us to connect professionals with this opportunity. As our Data Architect, you will establish the architecture, standards, and roadmap that help trusted data move effectively across our AWS-based products and platforms. You will bring together operational, analytical, and external data needs, working across software engineering, data engineering, analytics, product, security, and business teams to turn complex insurance data into durable, reusable data products.

last updated 40 week of 2026