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

Requirements

• 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.