getSummary
Written in English from the original postingThe role combines data engineering and advanced analytics, with responsibilities for building ETL/ELT pipelines, maintaining cloud data warehouses and data lakes, and implementing data quality and governance practices. It also includes querying large datasets with SQL and Python and creating automated dashboards and BI reports. The posting requires 4 to 6+ years of combined data engineering and analytics experience, advanced SQL/Python/Scala, cloud platforms, orchestration tools such as Airflow or dbt, and BI tools such as Tableau or Power BI.
Job description
Data Engineering: Design, build, and optimize robust ETL/ELT data pipelines from multiple data sources.
Architecture & Storage: Maintain cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) and data lakes.
Data Quality: Implement data governance, validation checks, and error-handling routines.
Advanced Analytics: Query large datasets using complex SQL and Python to extract business value.
Visualization & Reporting: Create automated dashboards and BI reports (Tableau, Power BI) for stakeholders.
Requirements
Experience: 4 to 6+ years of combined experience in data engineering and advanced data analysis.
Programming: Advanced SQL, Python, or Scala.
Big Data & Cloud: Experience with AWS, GCP, or Azure; familiarity with Spark or Hadoop.
BI Tools: Expertise in Power BI or Tableau.
Orchestration: Hands-on practice with tools like Airflow or dbt.
Education: Degree in Computer Science, Engineering, Statistics