We are seeking an experienced Cloud Native AWS Data Architect to lead the design and implementation of modern data platforms and analytics solutions for the insurance industry. The ideal candidate will bring strong expertise in AWS data services, big data processing, data modeling, enterprise analytics, and cloud-native architectures.

This role requires a hands-on technical leader who can drive data architecture decisions, mentor engineering teams, and ensure delivery of secure, scalable, cost-optimized, and high-performance data solutions supporting reporting, dashboards, advanced analytics, and data products.

Bachelor’s degree in Computer Science, Software Engineering, Mathematics, or related field; Master’s preferred.8+ years of progressive experience in data engineering / analytics engineering / platform engineering, with 3+ years in a Data Architect / AWS Data Architecture leadership role.Proven experience designing and implementing AWS-native analytics platforms, data lakes/lakehouse, and enterprise-scale BI/analytics architectures.Strong hands-on expertise with AWS data services including S3, Glue, Athena, Redshift, EMR, Lake Formation, RDS/Aurora, and orchestration tools like Step Functions / Airflow / MWAA.Experience with big data processing using Spark / PySpark, and modern data engineering approaches (partitioning, compaction, incremental processing, CDC patterns).Deep proficiency in SQL, performance tuning, and scalable design for analytical workloads.Strong understanding of data modeling , metadata/catalog strategies, and data governance concepts.Strong understanding of multi-tenant architectures and secure data isolation strategies for analytics workloads.Familiarity with Agile/Scrum methodologies and experience collaborating across product, engineering, and data teams.Knowledge of infrastructure-as-code tools such as Terraform, CloudFormation, or CDK.Strong communication skills, ability to influence stakeholders, and ability to drive architecture across multiple teams.

Preferred Skills

Expertise building scalable S3-based Data Lakes / Lakehouse architectures and open table formats (Apache Iceberg / Delta Lake / Hudi).Strong experience with Amazon Redshift (RA3 / Serverless) including performance tuning, workload management, Spectrum, and cost optimization.Proven ability to design and implement data pipelines using AWS Glue, EMR/Spark, Athena, Step Functions including batch and event-driven ingestion patterns.Experience integrating RDS / Aurora (PostgreSQL/MySQL) into analytics ecosystems, including replication/CDC and OLTP-to-analytics data flows.Familiarity with BI platforms such as Power BI, Tableau, QuickSight, Looker, and semantic modeling approaches.Understanding of data governance practices including data classification, stewardship, lineage, and access controls 

 

#LI-MV2

Lead the architecture, design, and implementation of cloud-native data platforms — including ingestion, storage, processing, metadata management, governance, and analytics enablement.Define and own data architecture standards, including patterns for data lakes, lakehouse, data warehousing, data marts, and scalable analytics solutions on AWS.Collaborate with product management, business stakeholders, and cross-functional teams to translate business requirements into data products and analytics solutions.Drive domain-driven data modeling, including canonical data models, dimensional models, and entity relationships aligned to business domains.Design and implement scalable data pipelines (batch and streaming) using AWS and modern big-data frameworks (e.g., Spark, EMR, Glue, Athena, Step Functions).Architect data solutions leveraging S3-based Data Lake/Lakehouse patterns using open formats like Parquet / Iceberg / Delta (where applicable).Build and govern enterprise analytics ecosystems, including semantic layer design, KPI definitions, and reusable curated datasets for BI and downstream consumers.Design and optimize data warehouses and analytical query platforms using Amazon Redshift (RA3/Serverless), Athena, and federated query patterns.Ensure robust data quality, lineage, observability, and monitoring, integrating with tools/services such as CloudWatch, Dynatrace, data pipeline metrics, and automated validation checks.Implement security best practices in data architecture including IAM, encryption (KMS), row/column-level security, PII handling, data masking, and regulatory compliance controls.Familiar with version-controlled pipelines, CI/CD automation, environment promotion, and infrastructure-as-code (Terraform/CloudFormation/CDK).Architect event-driven and near-real-time analytics solutions using services such as Kafka/MSK, Kinesis, SQS/SNS, and streaming ingestion frameworks.Conduct proof-of-concept initiatives to evaluate emerging AWS services and tools that improve performance, cost, and developer productivity.Mentor engineering and analytics teams on architecture best practices, query performance optimization, modeling standards, and scalability patterns.