Data Engineer / BI Developer – GCP, dbt & Power BI

Role overview

Responsibilities

• Design, develop, and maintain scalable pipelines using Apache Airflow; develop and optimize data warehouses using Google BigQuery.
• Build reusable dbt transformations, models, tests, documentation, macros, and incremental processing logic.
• Create Power BI reports, dashboards, semantic models, relationships, measures, calculated columns, and DAX calculations.
• Write optimized SQL and maintain dimensional models, fact tables, views, and reporting datasets.
• Monitor Airflow DAGs; troubleshoot data pipelines and data-quality issues; optimize BigQuery performance and cost.
• Perform source-to-target validation and reconciliation, implement data-quality checks, and support reviews, deployments, testing, and production.
• Maintain technical documentation for pipelines, transformations, datasets, and dashboards.

Required skills and experience

• Two to four years of Data Engineering, Business Intelligence, or Analytics Engineering experience; the description also states 3–5 years in its experience heading.
• Hands-on Google BigQuery, Apache Airflow DAG, dbt Core or dbt Cloud, SQL, and Power BI experience.
• Knowledge of Power Query, DAX, data modeling, Power BI Service, semantic model refresh, and row-level security.
• Understanding of ETL/ELT architecture, data warehousing, fact and dimension models, and star-schema reporting.
• Knowledge of data validation, reconciliation, and troubleshooting; experience with Git or similar version control.

Additional advantageous skills

• Snowflake; Databricks, Apache Spark, or Lakehouse architecture; Python for data engineering or automation.
• Additional GCP services, Azure or AWS, CI/CD pipelines, data governance, data quality, metadata management, security concepts, and Agile/Scrum are listed as advantages or good to have.
• Candidates with broader modern data-platform exposure, particularly Snowflake and Databricks, are preferred.