Principal Data Architect

- 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.

We are looking for a Data Architect to establish the architecture, standards, and roadmap that enable trusted data to move effectively across our AWS-based products and platforms. You will connect operational, analytical, and external data needs, defining how data is modeled, integrated, governed, secured, and consumed. The role requires a practical architect who can work across software engineering, data engineering, analytics, product, security, and business teams to turn complex insurance data into durable, reusable data products. - Define the target data architecture and phased roadmap for operational systems, data lakes, warehouses, integration services, analytics platforms, and customer-facing data products.

- 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.

- 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

- Provide architecture oversight from discovery through production and coach data engineers, software engineers, analysts, and technical leads in effective data design - 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.

- 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.

- 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.

- 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.

- 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.

- 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.