Role & responsibilities

• Data Modeling and Transformation:
  Design, build, and maintain robust and scalable data models using dbt and SQL. Create reusable transformations and ensure data is structured for efficient analysis.
• Pipeline Development:
  Develop and optimize ELT pipelines, leveraging dbt for transformations and Databricks for compute and orchestration when needed.
• Quality Assurance:
  Implement and manage data quality tests within dbt to ensure data integrity, accuracy, and consistency.
• Documentation and Governance:
  Maintain comprehensive documentation for dbt models, data lineage, and dependencies.
• CI/CD and Version Control:
  Apply software engineering best practices using Git and set up CI/CD pipelines for automated dbt project deployments.
• Collaboration:
  Partner with data analysts, data scientists, and business stakeholders to deliver clean, reliable datasets for BI and ML use cases.

Essential Skills

Technical Skills:

• dbt (Primary):
  Hands-on expertise with dbt Core and dbt Cloud, including writing models, tests, macros, and using the CLI.
• SQL:
  Expert-level proficiency in writing complex, efficient SQL queries for data transformation.
• Databricks (Secondary):
  Experience with Azure Databricks for data processing and orchestration. Familiarity with Spark is a plus.
• Azure Data Platform:
  Knowledge of Azure Synapse Analytics, Azure Data Lake Storage, and Azure Data Factory.
• Programming:
  Proficiency in Python for scripting and pipeline automation. Familiarity with Jinja for dbt templating.
• Data Warehousing:
  Strong understanding of dimensional modeling, star/snowflake schemas, and data warehousing concepts.
• Version Control & DevOps:
  Experience with Git and CI/CD tools (Azure DevOps or similar).

Preferred candidate profile