Assistant Manager - Azure Data Engineer
This role requires hands-on expertise in designing scalable data pipelines using Databricks and Spark to process large datasets, while also leveraging SQL and Python for data modeling and analytics. The Assistant Manager will collaborate on building KPI dashboards and ensuring seamless integration of data solutions within KPMG’s enterprise environment.
Skills required
About this role
Detailed knowledge of data warehouse technical architectures, data modelling, infrastructure components, ETL/ ELT and reporting/analytic tools and environments, data structures and hands-on SQL coding
Proficient in programming languages: Python
Experienced with Databricks designing, developing, and maintaining data pipelines and solutions using Databricks, Apache Spark, and other related tools (Unity Catalog, Workflow, Autoloader, delta sharing) to process large datasets and extract actionable business insights.
Databrick (e.g., DBX notebooks, SQL, Python/Spark, asset bundles, GIT, etc.)
Experience building metrics deck and dashboards for KPIs including the underlying data models.
Understand how to design, implement, and maintain a platform providing secure access to large datasets
Responsibilities
Experienced with Databricks designing, developing, and maintaining data pipelines and solutions using Databricks, Apache Spark, and other related tools (Unity Catalog, Workflow, Autoloader, delta sharing) to process large datasets and extract actionable business insights.
Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations
Proven track record partnering with business owners to understand requirements and developing analysis to solve their business problems
Proven analytical and quantitative ability and a passion for enabling customers to use data and metrics to back up assumptions, develop business cases, and complete root cause analysis
Qualifications
Bachelor’s degree in computer science, Data Science, engineering, mathematics, information systems, or a related technical discipline