Design and build scalable data pipelines, data products, and analytics solutions using Spark, Python, SQL, and modern data engineering frameworks.
Develop and optimize data engineering solutions on modern cloud data platforms and lakehouse architectures, such as Databricks, Microsoft Fabric, Snowflake, Sigma, or similar enterprise data platforms.
Build cloud-native data solutions using Azure services, including Azure Cloud, Azure DevOps, Azure Storage, and Azure Data Services.
Apply data modeling techniques and architectural patterns including data lake, lakehouse, Delta Lake, and data warehouse solutions.
Implement CI/CD pipelines, automated testing, and version control practices to enable fast, reliable deployments.
Design and support distributed data processing solutions using Apache Spark and related frameworks.
Collaborate with cross-functional teams to translate business and user requirements into robust data engineering solutions.
Support AI-enabled delivery by applying machine learning techniques, Retrieval-Augmented Generation (RAG), agentic AI frameworks, and modern AI engineering practices.
Leverage platform-native AI capabilities to improve data access, analytics, automation, and solution delivery.
Ensure data solutions are reliable, scalable, secure, and aligned with Tax Innovation standards and delivery methodology.
Champion continuous improvement across data engineering practices, workflows, and delivery processes.
Stay current with emerging data, AI, cloud, and platform capabilities that can transform how the Tax Innovation Data team delivers work.