SQL Developer

Overview

Strong SQL Developer with an understanding of Data Build Tool (DBT)

Data Transformation and Modeling:

• Design and implement data transformations and models using data to create structured, cleaned, and aggregated datasets.
• Develop and maintain data models that accurately represent business logic and data requirements.

SQL Expertise:

• Write and optimize SQL queries within dbt to extract, manipulate, and join data from various sources (e.g., databases, APIs, flat files).
• Ensure SQL code follows best practices for readability, performance, and maintainability.

Version Control:

• Use version control systems (e.g., Git) to manage dbt codebase, enabling collaborative development and tracking changes over time.
• Collaborate with data engineers and analysts to coordinate code changes.

Testing and Documentation:

• Implement unit tests within dbt to verify the accuracy and reliability of data transformations.
• Document dbt models, data lineage, and transformations to facilitate understanding and collaboration.

Automation:

• Schedule and automate dbt runs to keep data models up-to-date and synchronized with source systems.
• Implement data orchestration and scheduling as needed.

Data Quality Assurance:

• Develop and enforce data quality checks and validations within dbt to identify and rectify data issues.
• Monitor data quality and integrity, responding to anomalies or discrepancies.

Performance Optimization:

• Optimize dbt models and queries for performance, identifying and addressing bottlenecks.
• Analyze and fine-tune data processing pipelines to meet performance requirements.

Collaboration:

• Collaborate closely with data engineers, data analysts, and business stakeholders to understand data requirements and deliver data solutions.
• Participate in cross-functional teams and contribute to data-related projects.

Security and Compliance:

• Ensure data security and compliance with relevant data protection regulations (e.g., GDPR, HIPAA) through appropriate data handling practices.

Knowledge Sharing:

• Share knowledge of dbt best practices and data modeling techniques with team members.
• Provide training and support to data analysts and other users of dbt.

• Bachelor's or master's degree in computer science, data science, or a related field.
• Strong proficiency in SQL and experience working with relational databases.
• 3+ years of experience using dbt for data transformation and modeling in a data warehouse environment (e.g., Snowflake, Big Query, Redshift).
• 7+ years of experience building business rules using a business rules engine similar to dbt
• Familiarity with version control systems (e.g., Git) and code collaboration workflows.
• Excellent data analysis and problem-solving skills.
• Strong attention to detail and a commitment to data quality.
• Understanding of data warehousing concepts and best practices.
• Knowledge of data governance and data security principles.
• Effective communication and collaboration skills to work with diverse teams.
• Experience with other data tools and languages (e.g., Python, R, Looker) is a plus.