01Role 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 con .
