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High competition due to KPMG brand, common Data Engineer title, metro locations, and broad skills demanded.
Core data engineering skills are transferable, but Databricks/Azure specialization increases domain sensitivity.
Platform-specific Databricks/Azure, Spark, SQL, and Python requirements make shortlisting stringent.
Design, develop, and maintain data pipelines and solutions using Databricks, Apache Spark, and related tools to process large datasets and derive actionable insights.
Build metrics decks and dashboards for key performance indicators (KPIs) including developing underlying data models.
Design, implement, and maintain platforms for secure access to large datasets and collaborate with business owners to translate requirements into data-driven solutions.
Bachelor’s degree in computer science, data science, engineering, mathematics, information systems, or related technical discipline.
Detailed knowledge of data warehouse technical architectures, data modeling, ETL/ELT, reporting/analytics tools, and hands-on SQL coding.
Proficiency in Python and experience with Databricks (including DBX notebooks, SQL, Python/Spark, Unity Catalog, Workflow, Autoloader, delta sharing).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced with end-to-end data engineering using Databricks and Apache Spark in large data environments.
A data engineer capable of both technical development (pipelines, models) and collaborating with business stakeholders to implement data solutions.
Skilled in building metrics frameworks and dashboards to support business KPIs and analytics-driven decision making.