• Lead end-to-end delivery of data platform initiatives with full ownership of quality and documentation • Partner directly with stakeholders to define requirements and proactively communicate scope or technical impacts • Operate autonomously, escalating blockers with recommended solutions • Drive technical decisions and mentor peers on platform design and engineering best practices • Represent the engineering team confidently in cross-functional and stakeholder discussions

• Own data engineering projects end-to-end, ensuring quality and thorough documentation • Communicate directly with business and technical stakeholders to gather requirements and proactively manage scope changes • Work independently with minimal supervision while identifying blockers early and proposing solutions • Provide technical guidance and influence architecture and design decisions • Represent the engineering team professionally during stakeholder interactions

• Lead the architecture and development of scalable, secure data pipelines supporting AI/ML workloads • Own end to end data engineering processes: ingestion, transformation, storage, quality, and monitoring • Collaborate with data scientists and ML engineers on model features, training pipelines, and deployment • Drive best practices in data modeling, orchestration, versioning, and performance optimization • Mentor and guide junior engineers • Ensure data governance, lineage, and compliance standards are met across platforms • Support real time and batch processing frameworks in production environments

• Lead Financial data modeling and data product delivery • Oversee the design and buildout of ETL/ELT data pipelines • Implement best practices for performance tuning and data governance • Create documentation for data pipelines, workflows, and processes • Share best practices and mentor junior data engineers