Sr Data Engineer - Guardrisk MMH261102-2

Role Purpose

The incumbent is responsible for designing, developing and optimising data engineering

solutions on the Azure Databricks platform using PySpark, implementing medallion

architecture patterns (Bronze, Silver, Gold), scheduling and orchestrating data workflows,

and delivering quality analytics and reporting solutions to the business

Requirements

• Exposure to the full data engineering and analytics development life cycle.
• 5+ yrs of core experience in Data Engineering, Data Analytics, or Business Intelligence.
• Azure Databricks and PySpark experience essential; experience with Databricks
  
  

Workflows (job scheduling and orchestration) required

• Strong knowledge of PySpark, Python, SQL, and data pipeline orchestration using
  
  

Databricks Workflows

• Good understanding of medallion architecture (Lakehouse), Delta Lake, data
  
  

modelling, and supporting areas (Data Transformation, Governance and Reporting)

• Power BI or Databricks SQL for reporting and analytics preferred
  
  

Duties and Responsibilities

• Participate in the analysis, design, development, troubleshooting and support of the
  
  

enterprise Databricks data platform and analytics environment

• Design, build, test and implement data pipelines using PySpark and Databricks
  
  

notebooks, following medallion architecture patterns (Bronze, Silver, Gold) to

transform raw data into curated, analytics-ready datasets

• Develop and maintain data solutions using PySpark, SQL, Databricks Workflows, Unity
  
  

Catalog and Delta Lake on the Azure Databricks platform

• Integrate with diverse source systems (including but not limited to: In-House, Vendorbased, On-prem and Cloud-based, and Office 365)
• Configure, schedule and monitor Databricks Workflows (jobs) to ensure reliable and
  
  

timely data processing, and collaborate with DevOps on CI/CD practices for notebook

and pipeline deployments

• Responsible for the day-to-day data engineering tasks including developing and
  
  

optimising PySpark transformations, managing Delta tables, and maintaining data

quality across the medallion layers

• Maintain and evolve the medallion architecture data models, Unity Catalog
  
  

governance structures, and analytics-ready Gold layer datasets

• Apply Spark performance tuning techniques (partitioning, caching, broadcast joins,
  
  

cluster sizing) to optimise data pipeline throughput and cost efficiency

• Engage directly with business stakeholders to gather requirements, provide datadriven recommendations, and translate business needs into technical solutions. Assist
  
  

lead developer in Coordinate team efforts to achieve business objectives (Strategic

and operational)

• Ensure business continuity documentation through Azure DevOps
• Enforce data security and governance standards through Unity Catalog, access
  
  

controls and data classification, ensuring adherence by all team members

• Review code implementations, PySpark notebooks and pipeline designs by the Data
  
  

team, ensuring adherence to best practices and coding standards

• Oversee quality of data engineering solutions by junior team members, providing
  
  

constructive feedback and guidance

• Actively mentor Junior and Intermediate team members, sharing knowledge on
  
  

Databricks, PySpark, medallion architecture and data engineering best practice

Drive implementation of innovative data platform capabilities and Databricks features

(e.g., Delta Live Tables, Databricks SQL, ML integrations) through the Lead Developer

and Enterprise Architecture team

• Utilize junior members in achieving large scale project developments
  
  

and implementations in consultation with the lead developer

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