Data Engineer - Guard Risk

Introduction The founding of Metropolitan dates to as early as 1898 and Momentum traces its roots to being established in 1966. Momentum Group was formed from the merger of Metropolitan and Momentum, two sizeable and diverse insurance-based financial services companies in South Africa and was listed on the Johannesburg Stock Exchange on 1 December 2010. In order to grow its international footprint and diversify its revenue streams, Momentum Group actively pursues financially viable opportunities across the world. Aditya Birla Health Insurance Company Limited, a joint venture with Aditya Birla Capital Limited, was incorporated in 2015 and was our first expansion into India. Having experienced India first-hand as a hub of both talent and opportunity, we set up Momentum Services in 2017, a wholly-owned Mumbai-based global in-house centre to leverage IT, ITES and Business support services to all our businesses worldwide. 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. Duties & Responsibilities Participate in the analysis, design, development, troubleshooting and support of the enterprise Databricks data platform and analytics environmentDesign, 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 datasetsDevelop 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, Vendor based, 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 deploymentsResponsible for the day-to-day data engineering tasks including developing and optimising PySpark transformations, managing Delta tables, and maintaining data quality across the medallion layersMaintain and evolve the medallion architecture data models, Unity Catalog governance structures, and analytics-ready Gold layer datasetsApply 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 data driven 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 DevOpsEnforce data security and governance standards through Unity Catalog, access controls and data classification, ensuring adherence by all team membersReview code implementations, PySpark notebooks and pipeline designs by the Data team, ensuring adherence to best practices and coding standardsOversee quality of data engineering solutions by junior team members, providing constructive feedback and guidanceActively mentor Junior and Intermediate team members, sharing knowledge on Databricks, PySpark, medallion architecture and data engineering best practices 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 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 Competencies Mandatory Skills: Techn .