Lead a two-month project to assess the current data environment and design, build, and optimize a scalable cloud data warehouse, implement automated ETL/ELT pipelines, develop dimensional analytics-ready datasets, ensure data quality and performance, document architecture and deliverables, and report progress to the CEO.
LearnTastic is looking for an experienced Data Engineer to lead a two-month project to assess our current data environment and design, build, and optimize a scalable cloud data warehouse. This role will be responsible for recommending the overall data architecture, building reliable ETL/ELT data pipelines, and creating analytics ready data sets that enable business intelligence and reporting. You will work closely with data analysts, and business stakeholders, and the CEO to establish and deliver a scalable foundation for future data and analytic initiatives.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.
- 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.
- 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
Location: Remote
Contract Duration: 2 months
Reporting Line: CEO
Contract Duration: 2 months
Reporting Line: CEO
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