Data Engineer – Build Scalable Data Pipelines

About the role

We are seeking a Data Engineer to design, develop, and maintain robust ETL/ELT pipelines that power our data‑driven initiatives. The role will focus on building scalable data architectures, ensuring data quality, and working closely with analysts and business teams to translate data requirements into reliable solutions.

Key responsibilities

• Design, develop, and maintain ETL/ELT pipelines for data ingestion and transformation.
• Build and optimise scalable data architectures, data warehouses, and data lakes.
• Develop and manage data models and databases.
• Ensure data quality, integrity, security, and governance across all systems.
• Monitor pipeline performance and troubleshoot issues.
• Collaborate with data analysts and business stakeholders to understand and fulfil data requirements.
• Implement data integration from multiple internal and external sources.
• Automate data workflows to improve processing efficiency.
• Create and maintain technical documentation for data systems and processes.

Required profile

• Strong experience with SQL and relational databases (MySQL, PostgreSQL, SQL Server, Oracle, Snowflake).
• Hands‑on experience with ETL tools and frameworks.
• Proficiency in programming languages such as Python, PySpark, Scala, or Java.
• Experience with big‑data technologies like Apache Spark, Kafka, Airflow, and Hadoop.
• Familiarity with cloud platforms (AWS, Azure, or Google Cloud Platform).
• Knowledge of data‑warehousing concepts and dimensional modeling.
• Comfort with version control systems (Git) and CI/CD pipelines.
• Strong analytical and problem‑solving abilities.

Required skills

• SQL
• MySQL
• PostgreSQL
• SQL Server
• Oracle
• Snowflake
• ETL tools
• Python
• PySpark
• Scala
• Java
• Apache Spark
• Kafka
• Airflow
• Hadoop
• AWS
• Azure
• Google Cloud Platform
• Git
• CI/CD
• DevOps
• Data warehousing
• Dimensional modeling