Data Engineer

At Rakbank, we're transforming the way data drives decision-making across the organization. We are looking for a talented Data Engineer to join our growing Data & Analytics team and help build scalable, reliable, and secure data solutions that power analytics, reporting, regulatory, operational, and AI-driven initiatives. The role is based within our Information Technology division and reports to the Lead Data Engineer. What You Will Do Design, build, deploy, and maintain scalable data pipelines on the Databricks Lakehouse platform. Develop and optimize data ingestion, transformation, and enrichment processes across batch, streaming, and near-real-time workloads. Build and maintain curated datasets and data models that support business intelligence, analytics, regulatory reporting, and AI use cases. Implement data quality controls, monitoring, reconciliations, and automated testing frameworks. Support migration of legacy data solutions into modern cloud-based data platforms. Troubleshoot production issues, perform root-cause analysis, and continuously improve platform reliability. Collaborate with architects, analysts, governance teams, and business stakeholders to deliver trusted data products. Contribute to Agile delivery practices using Azure DevOps, CI/CD, and engineering best practices. What You Will Bring 3-7 years of experience in Data Engineering, Analytics Engineering, Software Engineering, or a related field. Strong hands-on experience with SQL, Python, PySpark, and Spark SQL. Experience working with Databricks, Delta Lake, Databricks Workflows, and Databricks SQL. Knowledge of Azure Data Factory (ADF), dbt, CDC frameworks, and streaming technologies such as Kafka/Confluent. Understanding of dimensional modelling, medallion architecture, data quality frameworks, and data governance principles. Familiarity with Git, CI/CD practices, and Agile delivery methodologies. Strong problem-solving, collaboration, and communication skills. Banking or financial services experience is advantageous. Azure or Databricks certifications will be an added advantage.

3-7 years of experience in Data Engineering, Analytics Engineering, Software Engineering, or a related field. Strong hands-on experience with SQL, Python, PySpark, and Spark SQL. Experience working with Databricks, Delta Lake, Databricks Workflows, and Databricks SQL. Knowledge of Azure Data Factory (ADF), dbt, CDC frameworks, and streaming technologies such as Kafka/Confluent. Understanding of dimensional modelling, medallion architecture, data quality frameworks, and data governance principles. Familiarity with Git, CI/CD practices, and Agile delivery methodologies. Strong problem-solving, collaboration, and communication skills. Banking or financial services experience is advantageous. Azure or Databricks certifications will be an added advantage.

Design, build, deploy, and maintain scalable data pipelines on the Databricks Lakehouse platform. Develop and optimize data ingestion, transformation, and enrichment processes across batch, streaming, and near-real-time workloads. Build and maintain curated datasets and data models that support business intelligence, analytics, regulatory reporting, and AI use cases. Implement data quality controls, monitoring, reconciliations, and automated testing frameworks. Support migration of legacy data solutions into modern cloud-based data platforms. Troubleshoot production issues, perform root-cause analysis, and continuously improve platform reliability. Collaborate with architects, analysts, governance teams, and business stakeholders to deliver trusted data products. Contribute to Agile delivery practices using Azure DevOps, CI/CD, and engineering best practices.