• Build and maintain Kafka ingestion pipelines • Define topic structures, partition strategies, retention policies, and consumer logic for multi-tenant setups • Manage data contracts and schema evolution • Develop idempotent ingestion services that land data into Iceberg tables • Design and optimize Iceberg tables (partitioning, compaction, clustering, retention rules) • Work with Nessie branches/tags to manage multi-environment (dev/test/prod) and multi-MNO deployments • Develop Spark jobs (batch + micro-batch where needed) for cleaning and normalizing events, categorizing senders, and identity stitching and grouping • Build dbt models on top of Iceberg datasets via Dremio and dbt

• Establishes a technical roadmap for the platform and/or capability strategy and lifecycle that considers value-based outcomes, costs to maintain, supportability, and performance. • Ensures sound integration, data, security, and business architecture design throughout all stages within the platform and/or capability lifecycle. • Provides rapid delivery and development of technical solutions that align with business and/or platform desired outcomes. • Troubleshoots and resolves technical issues related to platform or capability systems, solutions, and services. • Innovates and drives continuous improvements of implementation methodology and technical service offerings based on customer/employee experiences or other enterprise objectives/outcomes. • Participates in a Community of Interest for engineers across all capability and platform teams to share information and strengthen understanding of business needs and technology-based business solutions. • Develops and maintains deep technical knowledge and expertise related to domain area systems, solutions, services, and applications.

• Assess and inventory existing legacy data in application data stores, shared folders and shared drives. • Assess the quality, completeness, and relevance of data to be migrated. • Identify and document data dependencies, redundancies, and inconsistencies. • Collaborate with stakeholders to understand data structures, relationships, and required formats for successful migration. • Develop a comprehensive data migration plan that outlines steps, timelines, and resource requirements for the migration process. • Identify potential challenges and propose solutions to mitigate risks associated with data migration. • Interpret legacy APIs and other system integrations, and define mapping between systems. • Plan for thorough testing of the migrated data to ensure accuracy, completeness, and successful integration. • Recommend validation reports and the process to document any discrepancies and work closely with stakeholders to resolve issues. • Collaborate with software engineers, database administrators, and development teams to ensure compatibility and alignment with the overall architecture. • Maintain effective communication with project stakeholders to provide updates on migration planning progress and gather feedback. • Create and maintain technical documentation related to planned data migration processes, procedures, and system configurations. • Specify reports on migration progress, data quality metrics, and issue resolution.

• Owns organizational-wide data architecture, defining standards, patterns, and designs that our teams will implement. Solves complex data challenges regardless of perceived ambiguity or degree of clarity. • Reviews data-related designs and implementations across teams for architectural consistency, performance, and scalability. • Produces reference implementations, proofs of concept, and hands-on guidance when teams encounter complex data engineering challenges. • Designs and develops data pipelines, integrations, and platform features with performance and scalability in mind. • Builds and maintains data APIs and ingestion systems that can handle complex, high-volume data efficiently. • Takes responsibility for the quality of data systems across the organization, including testing strategies and data integrity standards. • Owns the data architecture strategy across the platform, including database design patterns, data API standards, and data flow architecture. • Consults with product managers to define, scope, and plan new data features and capabilities. • Partners with the CTO and engineering leadership on strategic data initiatives and long-term architectural direction. • Partners with teams to define data quality standards, automated data validation patterns, and testing strategies for data pipelines. • Partners with engineering and product teams to design and support data infrastructure for AI/ML initiatives, including model training pipelines, feature stores, and inference data flows. • Designs the IoT data ingestion and distribution architecture to support data from a significantly expanding device fleet, including replayability, aggregation, and real-time distribution patterns. • Tests, evaluates, and recommends technologies to improve our overall data infrastructure. • Produces excellent documentation.