The role involves bridging the gap between data engineering, data science, and business analytics to build scalable data solutions. The individual will be responsible for understanding end-to-end data flows, from creation and ingestion to transformation and delivery. Key responsibilities include building curated data models, performing reconciliation-style validation, and ensuring financial data reliability through monitoring and testing. The position requires collaborating with cross-functional teams to translate business requirements into durable data models and engineering improvements. Additionally, the role involves developing abstractions to support scalable workflows and debugging internal data applications.

The role requires a strong understanding of modular data model design, such as star or snowflake schemas, and proficiency in advanced SQL. Candidates must have expertise in scripting, automation, and Object-Oriented Programming, along with experience managing ETL/ELT pipelines and orchestration tools like Airflow. Proficiency in data visualization tools and modern data warehouse architectures like Snowflake or Databricks is necessary. Furthermore, the position demands expertise in prompt engineering for LLMs and the ability to responsibly use generative AI tools in daily workflows.