Business Transformation Consultant-Data Analytics

Responsibilities

The candidate will lead the design and implementation of a modern data platform leveraging Databricks and DBT, driving migration and transformation initiatives. This includes migrating data warehousing componentssuch as ETL/ELT scripts and datasetsfrom legacy systems like Teradata and MS SQL to Databricks. The role also involves architecting comprehensive data governance frameworks encompassing data lineage, metadata management, and data quality processes. The candidate will collaborate with cross-functional teams to ensure scalable, high-performance data solutions aligned with business objectives.

Lead GenAI (Azure Open AI based) experiment lab for production-graded solutions. Imbibe LLM-based solutions into Data Platform migration initiatives. Serve as the single point of contact for model risk management from a financial services regulation perspective. The candidate will collaborate with cross-functional teams to ensure scalable, high-performance data solutions aligned with business objectives

Required education
Bachelor's Degree

Preferred education
Master's Degree

Required technical and professional expertise

Strong communication and leadership skills, with experience in stakeholder engagement and project delivery.

Proficiency in SQL and experience with data warehousing platforms such as Teradata and MS SQL.

Expertise with Databricks for advanced data processing and analysis. Proven ability to design and implement ETL/ELT workflows in complex environments.

Deep understanding of data governance concepts, including data lineage, metadata, and data quality.

Experience with leading GenAI (Azure Open AI based) experiment labs. Knowledge of incorporating LLM-based solutions into data platform migration initiatives

Preferred technical and professional experience

Strong knowledge of DBT (Data Build Tool) for transformation pipeline implementation.

Experience with cloud platforms like AWS, Azure, or Google Cloud for data services.

Familiarity with model risk management in the context of financial services regulation