Software cum Data Engineer (AMK)

We are looking for a Software cum Data Engineer to develop software applications and build reliable data pipelines. The role combines application development, system integration, and data engineering to support business operations, reporting, and analytics.

Key Responsibilities

• Design, develop, test, deploy, and maintain software applications and backend services.
• Build and maintain APIs to integrate applications, databases, and external systems.
• Develop and maintain ETL/ELT pipelines to extract, transform, and load data from multiple sources.
• Design and optimise database schemas, data models, and SQL queries.
• Implement data validation, cleansing, reconciliation, and monitoring to ensure data quality.
• Support data warehouses and datasets used for reporting, dashboards, and analytics.
• Troubleshoot application defects, pipeline failures, integration issues, and performance bottlenecks.
• Implement appropriate access controls, logging, and secure handling of data.
• Automate development, deployment, and routine data processing tasks.
• Work with business users, analysts, and technical teams to translate requirements into technical solutions.
• Maintain technical documentation, including application designs, data flows, deployment guides, and support procedures.
• Participate in code reviews and continuous improvement of software and data engineering practices.

Requirements

• Degree in Computer Science, Information Technology, Software Engineering, Data Engineering, or a related discipline.
• At least 3 years of relevant experience in software development and data engineering.
• Hands-on programming experience in Python and at least one application development language, such as Java, C#, or JavaScript/TypeScript.
• Strong SQL skills and experience with relational databases such as PostgreSQL, Microsoft SQL Server, MySQL, or Oracle.
• Experience developing backend applications, REST APIs, and system integrations.
• Experience building ETL/ELT pipelines, performing data transformations, and implementing data quality checks.
• Understanding of data modelling, database design, and data warehouse concepts.
• Familiarity with Git, automated testing, and CI/CD practices.
• Strong troubleshooting, analytical, and communication skills.

Good to Have

• Experience with AWS, Microsoft Azure, or Google Cloud.
• Exposure to tools such as Apache Airflow, dbt, Azure Data Factory, AWS Glue, or equivalent.
• Experience with large-scale data processing using Apache Spark or similar technologies.
• Familiarity with Docker, Kubernetes, and Infrastructure as Code.
• Exposure to Power BI, Tableau, or other business intelligence tools.