Data Architect for Advanced Analytics

2 months ago

Toronto, Ontario, Canada Tiger Analytics Full time

Tiger Analytics is a leading advanced analytics consulting firm, empowering Fortune 100 companies to unlock business value from data through our expertise in Data Science, Machine Learning, and AI.

We are seeking a talented **Data Engineer** to join our growing team. In this role, you will play a pivotal part in architecting, designing, and implementing cutting-edge analytics capabilities.

Responsibilities:

  • Design, build, and deploy large-scale data processing pipelines within a production environment.
  • Leverage your expertise in Python, SQL, and PySpark to create and optimize complex data processing and transformation pipelines.
  • Utilize Snowflake Cloud Datawarehouse and DBT tools to manage and process vast datasets.
  • Demonstrate advanced SQL knowledge and experience working with relational databases, query authoring (SQL), and various database systems.
  • Understand Data Warehouse (DWH) systems and migration strategies from DWH to data lakes/Snowflake.
  • Apply your understanding of ELT and ETL patterns to effectively transform data into desired models.
  • Analyze unstructured datasets and develop processes for data transformation, structures, metadata, dependency management, and workload optimization.
  • Collaborate effectively with cross-functional teams in a dynamic environment.

Qualifications:

  • 8+ years of overall industry experience, specifically in data engineering.
  • 5+ years of experience building and deploying large-scale data processing pipelines in a production setting.
  • Strong proficiency in Python, SQL, and PySpark.
  • Experience with Snowflake Cloud Datawarehouse and DBT tool.
  • Advanced working SQL knowledge and familiarity with various database systems.
  • Understanding of Data Warehouse (DWH) systems and migration strategies to data lakes/Snowflake.
  • Knowledge of ELT and ETL patterns and their appropriate application.
  • Strong analytical skills for working with unstructured datasets.
  • Experience in building processes for data transformation, structures, metadata, dependency management, and workload optimization.
  • Proven ability to collaborate effectively within cross-functional teams in a dynamic environment.

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