Analytics Engineer

Quick Facts

Role description: Design and maintain scalable data models and semantic layers. Build, optimize, and monitor ELT pipelines and data quality processes, ensuring documentation of business rules, data lineage, and transformations. Monitor data performance, availability, and SLAs and support BI/Analytics/Data Science/ML teams with reliable data products through cross-functional collaboration.

Responsibilities

• Design and maintain scalable data models and semantic layers
• Build, optimize, and monitor ELT pipelines and data quality processes
• Ensure documentation of business rules, data lineage, and transformations
• Monitor data performance, availability, and SLAs
• Support BI, Analytics, Data Science, and ML teams with reliable data products
• Collaborate with cross-functional teams to deliver business value

Requirements

• Strong knowledge of dimensional modeling, Medallion architecture, and semantic layer design
• Experience building ELT pipelines using tools such as Azure Data Factory, Airflow, Microsoft Fabric, or Databricks
• Advanced SQL, including query optimization and performance tuning
• Proficiency in Python, PySpark, and Spark SQL
• Experience with CI/CD, version control, and DataOps practices
• Knowledge of data quality, observability, governance, and lineage
• Experience with Power BI semantic modeling and DAX optimization
• Hands-on experience with cloud data platforms such as Snowflake, Synapse, BigQuery, Redshift, Delta Lake, or ADLS

Benefits

No benefits were provided in the text.