Joining an existing data engineering squad, you own Power BI semantic modeling on top of PySpark/SQL pipeline engineering, delivering enterprise-grade reporting and analytics. As an embedded Data Engineer, you turn PySpark pipelines into trusted Power BI models for the business.
Note: Shares a common PySpark/Snowflake base with "Data Engineer - Graphing" - source together, differentiate on semantic-modeling vs. graphing depth at interview.
Key responsibilities
1. Model semantically. Build and maintain Power BI semantic models (DAX, star schemas) for enterprise reporting.
2. Build pipelines. Develop PySpark/Python and SQL pipelines feeding the Snowflake setting.
3. Partner with stakeholders. Translate reporting requirements into performant data models.
4. Ensure data quality.
Validate accuracy and performance of models and underlying pipelines.
5. Collaborate. Operate inside the existing squad with no separate delivery lead required.
6. Explore GenAI. Apply GenAI techniques to reporting and data use cases as opportunities arise.
Must-have qualifications
- Python and PySpark
- SQL
- Power BI semantic modeling (DAX, data modeling)
Preferred
- Snowflake; Dataiku
- GenAI exposure
What success looks like – first 6 to 12 Months
- Power BI models adopted for key reporting use cases
- Reliable PySpark/SQL pipelines feeding those models
- Smooth integration into the existing squad
Confidential – Wissen Technology
Skills:- Data engineering, PowerBI, Python, PySpark and SQL
📌 Data Engineer Power BI (Bengaluru)
🏢 wissen technology
📍 Bengaluru
Reply to this offer
Impress this employer describing Your skills and abilities, fill out the form below and leave Your personal touch in the presentation letter.