Data Engineer (Two-Month Contract)

Responsibilities:

• Assess the current data environment and recommend a scalable cloud data warehouse architecture.
• Design, build, and optimize a cloud data warehouse and supporting data models.
• Build and maintain automated ETL/ELT pipelines from multiple data sources.
• Develop analytics ready data sets using dimensional modeling (star/snowflake schemas).
• Ensure data quality, integrity, and performance, and governance.
• Optimize SQL queries and database performance.
• Partner with data analytics and business stakeholders to support business intelligence and reporting.
• Document the data architecture, pipelines, and data models, and provide recommendations for future scalability.
• Provide regular project updates and recommendations to the CEO.

Must-Have:

• 5+ years of experience in Data Warehouse Engineering or Data Engineering, or a similar role.
• Strong SQL skills with experience optimizing complex queries and database performance.
• Hands-on experience with ETL/ELT pipelines.
• Experience designing and implementing cloud data warehouse solutions (e.g., Snowflake, Amazon Redshift, Google BigQuery, or Azure Synapse).
• Solid understanding of dimensional data modeling (e.g. star and snowflake schemas).
• Experience with Git and version control.
• Experience designing scalable data architectures.

Preferred:

• Experience with Python.
• Knowledge of dbt and data orchestration tools such as Apache Airflow, Azure Data Factory, or AWS Glue.
• Experience with AWS, Azure, or Google Cloud.
• Familiarity with BI tools such as Power BI, Tableau, QuickSight or Looker.
• Experience integrating data from APIs, SaaS applications, and relational databases.
• Strong analytical, problem-solving, and communication skills.

Expected Deliverables:

• Production-ready cloud data warehouse
• Automated ETL/ELT pipelines for agreed upon data sources
• Documented data models
• Optimised reporting datasets
• Technical architecture diagrams and documentation
• Data dictionary and pipeline documentation
• Knowledge transfer and handover
• Recommendations for future scalability