Software Engineer Ii

Job description

Build reusable frameworks and services for data ingestion, transformation, publishing, and data-product engineering, reducing bespoke implementations across teams. Develop platform capabilities that improve data discoverability, metadata management, ownership, lineage, readiness, quality, health, and access experiences. Build and enhance shared platform capabilities supporting the broader Marketing data control-plane strategy. Develop metadata-driven and configuration-driven engineering frameworks that reduce manual development and accelerate onboarding of new data products. Apply AI-assisted and agentic engineering techniques to code generation, migration, testing, documentation, metadata generation, schema mapping, and other data-engineering workflows. Explore and implement patterns for exposing trusted data and metadata to AI agents, MCP-based services, ontology-driven experiences, and other AI applications. Build reusable APIs, microservices, workers, and platform components that can be adopted across multiple Marketing data scenarios. Develop automated capabilities for data quality validation, testing, schema enforcement, monitoring, and operational health. Partner with Data Engineering, Platform Engineering, DataOps, Security/Governance, and product teams to deliver end-to-end platform solutions. Use telemetry, customer feedback, incidents, and usage data to continuously improve platform capabilities. Bachelor's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Experience with software engineering fundamentals, including data structures, APIs, distributed systems concepts, testing, and production-quality software development. Experience developing or operating data platforms, data pipelines, distributed data-processing systems, cloud applications, microservices, or event-driven architectures. Experience developing data ingestion, transformation, modeling, or publishing solutions, including reusable data-engineering frameworks, APIs, services, or internal platform capabilities. Experience using Git-based development, automated testing, CI/CD, and modern software-development practices. Experience with generative AI, agents, MCP, semantic/ontology models, AI-ready data-product patterns, and applying AI and automation to data-engineering workflows.