- Role Overview: Semantic Layer & Data Modelling (Python-first) Design and implement the
- Data/Semantic Layer
- Python
- frameworks to define business metrics, dimensions, and KPIs as code.
- Build the "Gold" or "Serving" layer in Databricks using
- PySpark, embedding complex business logic directly into the data pipeline rather than downstream BI tools.
- Implement
- Metrics-as-Code
- practices to ensure consistent definitions across different analytical tools (Power BI, custom apps, data science models).
- Translate functional business requirements into Python-based transformation logic for the final serving layer.
- Data Pipelines & Databricks Design, build, and maintain scalable batch and streaming pipelines using
- Databricks Workflows
- Apache Spark.
- Develop robust ETL/ELT processes to ingest data from diverse sources (IoT, ERPs, APIs) and move it through the Medallion Architecture (Bronze $o$ Silver $o$ Gold).
- Optimize Python/Spark code for performance, partition management, and cost efficiency.
- Architecture & Governance Contribute to the design of Lakehouse architectures, ensuring the semantic layer supports both self-service BI and advanced analytics.
- Implement data governance and quality checks within the Python pipeline code.
- Ensure the semantic layer aligns with Clients data security standards (Row-Level Security, masking) and access controls.
- Collaboration Work closely with Business Analysts to understand metric definitions and codify them in Python.
- Collaborate with Data Scientists to expose semantic features for Machine Learning models.




- Mentor junior engineers for data modeling.
- Minimum
- 5 years
- of professiIn this role, you will play a key role in designing and implementing clients next-generation data platforms.
- You will move beyond traditional ETL by focusing on the
- Semantic Layer, using
- Python
- to define complex business logic, metrics, and data models as code.
- You will bridge the gap between raw data and business consumption, ensuring consistent, governed data delivery via Databricks.

Your Responsibilities: onal experience in Data Engineering.

- Strong expertise in Python
- for data manipulation and modelling (pandas, PySpark).
- Proven experience building
- Semantic/Serving layers
- programmatically (not just drag-and-drop BI modeling).
- Experience with
- Databricks
- and the
- Delta Lake
- ecosystem.
- Familiarity with "Data-as-Code" or "Metrics-as-Code" concepts (e.g., using dbt with Python models, or custom Python semantic frameworks).
- Robust understanding of dimensional modeling (Star Schema) and how to implement it via Spark/Python.

Technical skills Languages:

- Python (Advanced), SQL (Strong).
- Core Platform:
- Azure Databricks, Apache Spark (PySpark), Delta Lake.
- Semantic/Modeling:
- Building serving layers in PySpark, dbt (Python models), or Python-based metric layers.

Data Architecture:

- Medallion Architecture (Bronze/Silver/Gold), Dimensional Modeling.
- DevOps:
- CI/CD for data pipelines (Azure DevOps/GitHub Actions), Git flow.
- Concepts:
- ACID transactions, Time Travel, Unity Catalog, Governance.

📌 Sr. Data Engineer (Databricks & Cloud Data Platforms) (Jaipur)
🏢 Datansh Solutions
📍 Jaipur