- Demonstrate capacity to clearly and concisely communicate complex business logic, technical requirements, and design recommendations through iterative solutions.

- Deep understanding of SQL in analytical data warehouse (we use Snowflake SQL) and exposure to business intelligence tools would be a plus (we use Tableau).

- Hands on experience working with SQL, performing ETL/ELT operations and building data pipelines (Using Azure or Kestra).

- Familiarity with Git and the command line.

- Deep understanding of relational and non-relational databases, SQL and query optimization techniques, and demonstrated ability to both diagnose and prevent performance problems (MS SQL and PostgresSQL).

- Effective communication and collaboration skills, including clear status updates.

- Comfort working in a highly agile, iterative environment.

Key Responsibilities :

- Design, develop, and maintain high-performance data pipelines and ETL/ELT workflows using platforms such as Azure Data Factory or Kestra.

- Clean, transform, and model large datasets from structured and unstructured data sources to enable analytics and reporting.

- Build and optimize data models and schemas for analytical data warehouses (we use Snowflake).

- Develop and manage dbt (data build tool) models to streamline transformation logic and ensure data consistency.

- Work with a wide range of data formats and sources, including HL7 (healthcare data), APIs, and flat files.

- Ensure data accuracy, performance, and scalability by applying best practices in data architecture and database optimization.

- Collaborate with cross-functional teams to understand data needs, translate business requirements, and deliver data-driven solutions.

- Use Git for version control and collaborate effectively in an agile, iterative development environment.

- Support analytics and reporting initiatives by enabling self-service BI tools (e.g., Tableau, Power BI).
- Continuously improve data engineering processes through automation, monitoring, and optimization.