- Extensive experience modelling designing, optimizing, and maintaining structured and semi-structured data models in any cloud data warehouse (e.g. in either BigQuery, Amazon Redshift, Microsoft Azure Synapse Analytics etc.) for analytical and operational use cases.

- Knowledge of cloud storage and processing optimizations (in either BigQuery, Amazon Redshift, Microsoft Azure Synapse etc.) , ensuring efficient data structuring for cost-effective querying and scaling.

- Strong understanding of normalized and denormalized data modelling techniques, including Star and Snowflake schemas, to support efficient querying and reporting.

- Proficiency in writing efficient SQL queries, using techniques like partitioning, clustering, and materialized views to enhance performance and cost-effectiveness.

- Ability to determine the right balance between normalization and denormalization to optimize query performance while maintaining data integrity.

- Expertise in defining and evolving schemas over time to accommodate business needs while minimizing disruptions and ensuring backward compatibility.

- Experience implementing data validation, consistency checks, and schema enforcement to maintain data accuracy, completeness, and reliability.

- Strong emphasis on data documentation, including entity-relationship diagrams (ERDs), data dictionaries, and lineage tracking to improve data discoverability and usability.

- Knowledge of BigQuerys storage and processing optimizations, ensuring efficient data structuring for cost-effective querying and scaling.

- Ability to work closely with data engineers, analysts, and business teams to align data models with business needs, ensuring best practices in data architecture