BI Engineer/BI工程师

What you'll do

The Manufacturing & Supply Chain hub team looks after the data and analytics needs of business areas across M&SC, largely through engagement with teams embedded within business functions. The D&A Delivery team’s responsibility within the hub will be to deliver data and analytical products following a prioritised backlog of use-cases, and hand over to our business units to manage them.

• Take accountability for use-case delivery, supporting Data Engagement Leads as required to advise on the art of the possible.
• Working as part of cross-functional teams, successfully deliver the analytical requirements in use-cases as prioritised by the Portfolio lead, and as part of a cross-functional team.
• Successfully support business areas in the usage of analytics products, as prioritised by Portfolio Lead.
• Advise business area teams on BI engineering best practice, helping them build products following the appropriate process.
• Provide technical guidance to business area teams on how to build data products in a way to facilitate reusability.
• Help facilitate in the upskilling of BI engineering resources across Volvo, contributing actively to the broader data and analytics community forums and events.
• Be a positive source of positivity within D&A, vocally supporting and encouraging the strategy and transformation.
• Proactively communicate the D&A vision and mission through informal channels.
• Embody the Volvo values and D&A cultural principles.

What you'll bring

• Bachelor's/master's in information management, Data Science, Computer Science or equivalent experience. 2-5 years BI Engineering experience.
• Data modelling expertise (Data Vault, Kimball, Data Lakehouse). ETL/ELT proficiency (Azure Data, AWS GLUE, Airflow, etc.).
• Advanced Power BI skills, other reporting tools are a plus.
• Experience with modern data platforms (Azure Synapse, Databricks, Snowflake).
• Strong SQL and Python skills. Cloud platform expertise. DevOps proficiency (Git, Agile, CI/CD).
• Fluent in English and Mandarin.
• Critical mindset and openness to feedback. Results-driven with commitment to excellence.
• Experience with dbt or data processing pipelines and data warehousing.
• Data mesh implementation expertise. Big data tools proficiency (Hadoop, Spark, Kafka)
• Knowledge of data ingestion methods (CDC, API, streaming) and formats (JSON, Parquet, Iceberg).