Join our dynamic Technology & Transformation practice and leverage your expertise in data to drive significant client value. We specialize in unlocking the potential hidden within vast datasets, providing strategic guidance and implementation services to transform disparate data sources into actionable insights. Our focus spans business intelligence, data management, performance management, and next-generation analytics including big data and machine learning.

This Senior Consultant role is pivotal in managing client relationships and ensuring the successful delivery of complex programs. You will serve as the main liaison between Deloitte and our clients, guaranteeing alignment on objectives, timelines, budgets, and expected results, ultimately fostering client satisfaction and driving business growth.

As a SAS framework data engineer, your responsibilities will include migrating SAS frameworks to Databricks/PySpark and designing/developing PySpark pipelines from SAS macros. You will implement validation frameworks between SAS and Spark outputs, and manage the DevOps lifecycle using Git/Bitbucket for code reviews and production deployments.

Key duties also involve designing, building, and maintaining scalable data pipelines on AWS, and collaborating with cross-functional teams to translate business requirements into technical delivery plans. You will build and optimize data lakes using S3, Glue Catalog, and Lake Formation, and work with Redshift/Snowflake for data warehousing solutions. Implementing data quality checks, monitoring, logging, and CI/CD deployments are also core to this role, alongside collaborating with analytics and business teams to deliver high-quality datasets.

We are seeking candidates with strong hands-on experience, particularly a minimum of 3 years in migrating SAS frameworks to Databricks/PySpark. Proficiency in Python (PySpark), SQL, and data transformation logic is essential.

Essential skills include experience with Redshift, Athena, and Glue Data Catalog, as well as knowledge of Spark (EMR/Glue) and distributed data processing. A good understanding of data modeling, data lake principles, and best practices is required. You should be adept at designing, developing, and deploying solutions using various tools and design principles. Familiarity with Terraform/CloudFormation for Infrastructure as Code is a significant advantage.

Additionally, you will need to understand existing processes, facilitate changing requirements through a structured change control process, and maintain proper documentation for solutions and test procedures. Coordinating with process owners to understand as-is processes and design automation workflows will be a key part of your role.