Distinguished Engineer, Data Platforms

Grade Level (for internal use): 13 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 enterprise data platform. Drive implementation of repeatable engineering patterns for ingestion, transformation, testing, deployment, and monitoring across onboarded datasets. 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 AWS Lambda for event-driven processing Amazon Kinesis for streaming use cases AWS Lake Formation for governed data lake controls 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. Influence architectural decisions related to Databricks, Delta Lake, Apache Iceberg, Unity Catalog, metadata-driven processing, and governed data lake design. AI-Assisted Engineering and Context Engineering Champion the adoption of AI-powered development tools, including  Claude, GitHub CoPilot, LLMs, and other AI-assisted engineering solutions , to increase engineering velocity, code quality, and operational effectiveness. Apply LLM-based tools to support common data engineering activities such as: Code generation Code refactoring SQL optimization PySpark optimization, etc. Develop and implement  context engineering  practices that provide AI tools with the necessary technical background, architectural constraints, data schemas, coding standards, platform patterns, security requirements, and governance expectations. Create reusable  context packs , prompt libraries, and AI-enabled engineering playbooks for common data platform tasks, including Databricks pipeline migration, ingestion framework development, test generation, code review support, and operational troubleshooting. Use LLMs to accelerate understanding and modernization of legacy pipelines, including dependency analysis, code explanation, transformation logic interpretation, metadata extraction, and refactoring recommendations. Data Onboarding and Asset-Agnostic Enablement Provide technical direction for offshore teams supporting data onboarding to the enterprise platform in an asset-agnostic manner. Define and operationalize onboarding patterns that can support a broad range of data assets, domains, and source systems without requiring bespoke platform redesign for each use case. Work with partner teams to simplify and standardize how data is ingested, transformed, governed, and published to the platform. Establish reusable ingestion and processing patterns across batch and streaming use cases using technologies such as AWS Glue, AWS Lambda, Amazon Kinesis, or event-driven integrations where appropriate. Data Mastering Platform Integration Support integration of platform pipelines and datasets with the enterprise data mastering platform. Collaborate with upstream and downstream stakeholders to ensure mastered data can be consumed reliably through standardized interfaces and governed data flows. Help establish data quality controls, reconciliation processes, metadata alignment, and stewardship workflows required to support trusted mastered data in the platform. Contribute to issue resolution and continuous improvement related to mastering-related ingestion and distribution workflows. Support data mastering capabilities aligned with platforms such as NeoXam DataHub, including: Data acquisition, Cleansing, Enrichment, Mastering, Reconciliation, Golden copy generation & Downstream distribution of trusted data products. Semantic Modeling and Data Discoverability Support semantic modeling practices that help create consistent business definitions, reusable data concepts, and governed consumption patterns across the enterprise data platform. Partner with business,