• Design, operate, and optimize global, multi-region database architectures across AWS and Azure. • Architect and manage relational, NoSQL, and document databases including Snowflake, PostgreSQL, DynamoDB, DocumentDB, Cosmos DB, and MongoDB. • Lead hands-on database migrations across cloud platforms (AWS ↔ Azure), legacy systems, and global regions. • Define and implement indexing strategies, query optimization techniques, and scaling patterns for large datasets and real-time workloads. • Tune databases for high availability, low latency, and cost efficiency at enterprise scale. • Support and optimize data ingestion and pipeline workflows built with AWS Glue, Step Functions, Lambda, ECS, and Azure Data Factory. • Partner with Data Engineering teams on streaming and CDC patterns using Kafka, Kinesis, or AWS DMS. • Develop and maintain secure API-based integrations and database connectivity patterns. • Implement database security best practices including IAM, secrets management, SSO, encryption at rest and in transit, and access controls. • Define and maintain backup and disaster recovery strategies including RTO/RPO planning and point-in-time recovery (PITR). • Implement robust observability for database systems using CloudWatch, Azure Monitor, Datadog, or similar tools. • Partner with Engineering, Analytics, Product, DevOps, and Security teams to deliver reliable, scalable data solutions. • Communicate complex database and architectural concepts clearly to both technical and non-technical stakeholders. • Influence engineering standards and best practices through technical leadership and collaboration. • Mentor engineers through design reviews, code reviews, and hands-on guidance.

• 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.

• Significant designing and coding of our market leading platform micro-services. • Build in quality and automated testing at all stages of development and deployment. • Adhere to the highest levels of agile scrum development and quality practices while delivering stories to the Definition of Done. • Understand and apply our corporate architectural strategy and standards whilst solving complex integration challenges. • Participate in the complete lifecycle of your software services from inception to L3 customer support. • Deploy and manage micro-services using Kubernetes on AWS cloud infrastructure. • Embrace a team culture of excellence and collaborate with a global network of platform development teams.

• Collaborate with data scientists to ensure data is accurately extracted, transformed, and loaded for analysis and decision-making • Effectively collaborate and partner with various Scores stakeholders to deliver data driven solutions that support strategic Scores initiatives • Ability to analyze, interpret, and manipulate large data sets to support analytic research and model development efforts • Ability to deliver high level results supporting business-critical projects within expected timelines • Use internal technologies in the development, maintenance and improvement of tools and processes to help solve challenging business problems in predictive analytics • Support our existing code base and the overall analytic SDLC • Demonstrate self-initiative and innovation by writing new code to continuously evaluate and improve existing code base • Apply advanced data transformation techniques to optimize the processing of large datasets • Work closely with the data scientists and other data engineers in constructing the best methodologies in generating new tools, code and datasets based on project requirements