Data Engineer – Build Scalable Data Pipelines

About the role

We are looking for a skilled Data Engineer to design, develop, and maintain scalable data pipelines and modern analytics platforms. The role involves close collaboration with business stakeholders, analysts, and engineering teams to deliver reliable, high‑quality data solutions.

Key responsibilities

• Design, build, and maintain ETL/ELT pipelines for structured and unstructured data.
• Develop and optimise data integration workflows across multiple sources and cloud platforms.
• Build and manage data warehouses, data lakes, and analytics platforms such as Snowflake, Redshift, or BigQuery.
• Ensure data quality, governance, and security throughout the data lifecycle.
• Optimise SQL queries and large‑scale processing jobs using Spark, Hadoop, or similar technologies.
• Collaborate with BI and analytics teams to understand reporting requirements.
• Implement data validation, monitoring, and error‑handling mechanisms.
• Automate workflows and deployments using CI/CD practices and version control.
• Troubleshoot and resolve production data issues promptly.

Required profile

• Bachelor’s degree in Computer Science, Software Engineering, IT or related field.
• 5–6 years of hands‑on data engineering experience.
• Strong analytical, problem‑solving and communication abilities.

Required skills

• SQL and database design concepts.
• Python, Scala or Java programming.
• ETL/ELT tools and pipeline development.
• Cloud platforms: AWS, Azure, GCP.
• Data‑warehousing solutions: Snowflake, Redshift, BigQuery, Synapse.
• Big data technologies: Apache Spark, Hadoop, Kafka.
• Orchestration tools: Airflow (or similar).
• Version control: Git.
• CI/CD and DevOps practices.
• Docker and Kubernetes (preferred).