• Own the technical roadmap for data infrastructure across AWS and Azure multi-cloud environments. • Drive architectural decisions for multi-region, cross-account data platforms supporting global operations. • Establish engineering standards and best practices for data pipelines, database design, reliability, and observability. • Lead incident post-mortems and drive systemic improvements that prevent entire classes of failures. • Influence product and engineering roadmaps by identifying technical risks, constraints, and opportunities. • Define capacity planning and cost optimization strategies to ensure performance, reliability, and financial efficiency at scale. • Architect greenfield systems for large-scale data migrations (e.g., Couchbase to DocumentDB or DynamoDB at TB+ scale). • Design fault-tolerant, self-healing data pipelines using AWS Glue, Step Functions, Lambda, ECS, and Azure Data Factory. • Define data modeling strategies across Snowflake, PostgreSQL, DynamoDB, and DocumentDB in multi-region environments. • Design observability into all systems, including monitoring, alerting, tracing, and debugging capabilities. • Own backup and disaster recovery strategies, including RTO/RPO planning, PITR, and cross-region replication. • Drive indexing, query optimization, and performance tuning for databases handling billions of documents. • Architect security patterns covering IAM, secrets management, encryption at rest and in transit, and access controls. • Partner with the Director of Data Engineering on strategic planning, prioritization, and roadmap execution. • Collaborate with senior leaders across Engineering, Product, and Analytics on enterprise technical strategy. • Work across organizational boundaries to deliver initiatives requiring coordination across multiple teams and time zones. • Translate business requirements into technical designs and clearly communicate trade-offs to non-technical stakeholders. • Represent the data organization in architecture reviews and cross-functional technical planning forums. • Lead by example through hands-on development of critical systems, migrations, and infrastructure. • Own and resolve complex production incidents affecting large-scale, distributed systems. • Drive cloud-to-cloud, legacy-to-modern, and cross-region data migrations. • Implement streaming and CDC solutions using Kafka, Kinesis, AWS DMS, and related technologies. • Build automated data quality, validation, and testing frameworks. • Optimize performance, reliability, and cost across critical workloads. • Automate operational workflows using Python, Infrastructure as Code, and cloud-native tooling. • Mentor Senior and Data Engineers through code reviews, design reviews, and technical guidance. • Raise the technical bar through documentation, standards, pairing, and knowledge sharing. • Help engineers grow from mid-level to senior and senior to staff through intentional coaching. • Promote a culture of operational excellence, including incident response, runbooks, and blameless post-mortems.