Data Engineer

Responsibilities:

Data Pipeline Development

• Develop data pipelines in Fivetran to extract data from common data sources.
  
• Write Python scripts, using libraries such as Pandas, for data cleaning and transformation.
  
• Create data models in DBT with transformations and joins between tables.
  

Data Quality and Governance

• Implement data quality checks in DBT and Snowflake to identify and address data quality issues.
  
• Follow established data governance practices, including access controls and documentation procedures.
  
• Monitor data pipelines for errors or inconsistencies, reporting issues to senior engineers.
  

Data Modelling and Design

• Design and document conceptual and logical data models for well-defined datasets, considering star schema or data vault principles.
  
• Apply data normalization techniques and select appropriate data types based on data characteristics.
  
• Collaborate with stakeholders to understand their data needs and identify potential model improvements.
  

Data Visualization Support

• Create data visualizations in QuickSight or Tableau to explore and communicate data insights.
  
• Select appropriate chart types and apply dashboard design principles for effective communication.
  

Delivery And Testing

• Take ownership of designing and implementing moderately complex data engineering tasks, identifying dependencies and risks during planning.
  
• Write integration tests to verify how code interacts with other parts of the data pipeline.
  

Data Security And Compliance

• Adhere to established data security and compliance protocols while handling data.
  
• Follow data access control procedures and complete required data security and compliance training.
  

Requirements

Must-have skills (required)

• Good understanding of data engineering concepts, data transformation techniques, and tools such as Fivetran, DBT, Snowflake, and QuickSight or Tableau.
  
• Proficient in Python, including libraries such as Pandas, for data cleaning and transformation.
  
• Experience building and maintaining data pipelines from common data sources.
  
• Understanding of data modelling methodologies and normalization principles for data warehousing.
  
• Experience implementing data quality checks and following data governance practices.
  
• Familiarity with data visualization tools and best practices for effective dashboards.
  
• Strong SQL skills for querying and transforming data.
  
• Good communication skills, able to collaborate with stakeholders on data requirements.
  

Academic And Professional Requirements

• Bachelor's degree in Computer Science, Data Engineering, or a related field, or equivalent practical experience.
  
• 2–4 years of experience in data engineering or a related role.