Key Responsibilities

·      Design, develop, and maintain scalable and reusable data models optimized for analytics and reporting use cases.

·      Build and manage data transformations using dbt, including tests, documentation, and performance optimization.

·      Implement and enforce data modeling best practices (star schema, dimensional modeling etc.).

·      Ensure data quality, consistency, lineage, and reliability through testing, validations, and monitoring.

·      Collaborate with analytics, product, and business stakeholders to understand and translate data requirements into robust models.

·      Work with modern cloud data warehouses, primarily Snowflake, to support analytical workloads.

·      Support BI and reporting needs as required, including troubleshooting data issues or enabling new datasets.

·      Contribute to data platform improvements, standards, and best practices across the data engineering team.

Must‑Have Skills (Core Technical)

·      Strong hands‑on experience with dbt (data build tool) for data transformations, testing, and documentation.

·      Advanced SQL skills with the ability to write efficient, maintainable analytical queries.

·      Solid understanding of data modeling concepts, including star schemas, dimensional modeling, and fact/dimension design.

·      Experience working with modern data warehouses, preferably Snowflake.

·      Strong focus on data quality, performance optimization, and reliability.

·      Ability to work closely with stakeholders and translate business requirements into analytical datasets.

Nice‑to‑Have Skills

·      Experience with BI and visualization tools such as Microsoft Power BI or Looker.

·      Ability to understand, support, and troubleshoot BI reports and dashboards.

·      Exposure to Microsoft Azure data services such as Azure Data Factory (ADF) and Databricks.

·      Familiarity with ELT/ETL orchestration, data governance, or metadata management.