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.
Datamesh implementation expertise.Big data tools proficiency (Hadoop, Spark, Kafka)
Knowledge of data ingestion methods (CDC, API, streaming) and formats (JSON, Parquet, Iceberg).