Director of Engineering, Data Platforms

About the Role: Grade Level (for internal use): 13We are seeking a strong technical and people leader to join as Director of Engineering, Data Platforms for our offshore team in India. In this role, you will partner closely with the core Databricks team to support the transition of existing data pipelines to the target platform, while continuing to refine, enhance, and operationalize those pipelines for scale, quality, reliability, and secure cloud-native delivery on AWS. AWS data engineering practices commonly emphasize automated and orchestrated data flows, metadata-driven pipelines, and alignment to platform guardrails and security controls.  This leader will manage a small, focused engineering team responsible for supporting offshore platform adoption and enabling asset-agnostic data onboarding into the enterprise data platform. The role is execution-oriented and collaborative in nature, with accountability for high-quality delivery, engineering rigor, and consistent partnership with global stakeholders. You will also support integrations with the enterprise data mastering platform to help ensure trusted, standardized, and reusable data flows across the ecosystem. Experience with NeoXam DataHub is a strong plus, particularly in environments where master, market, reference, risk, and investment data are managed across the full lifecycle—from acquisition through distribution—to create a trusted single source of truth. This role is ideal for someone who combines hands-on data platform depth with practical team leadership, strong cross-geography collaboration, and experience establishing repeatable cloud data engineering patterns across modern lakehouse and mastering ecosystems. Key Responsibilities Data Pipeline Transition and Platform Delivery Partner with the core Databricks team to plan and execute the transition of existing data pipelines to the target data platform. Lead the offshore team in enhancing, stabilizing, and optimizing pipelines after transition, with a focus on performance, scalability, maintainability, operational excellence, and secure AWS deployment patterns. Drive implementation of repeatable engineering patterns for ingestion, transformation, testing, deployment, and monitoring across onboarded datasets. Ensure pipelines are built and managed in a way that supports long-term platform consistency, reliability, observability, and ease of support. Guide the design and operation of cloud-native data pipelines leveraging relevant AWS services such as Amazon S3 for durable storage, AWS Glue for integration and catalog-driven processing, and AWS Lake Formation for governed data lake controls. AWS training and prescriptive guidance for data engineering emphasize building, optimizing, and securing solutions with these services. Promote the use of AWS IAM, encryption, and environment-level controls to enforce secure access to platform resources and data products in line with enterprise governance expectations.