This job is closed, but similar jobs are still open.

Key Responsibilities

Design, develop, and maintain ETL/ELT pipelines for structured and unstructured data.
Build data ingestion, transformation, integration, and processing workflows.
Develop data warehouses, data lakes, and/or lakehouse solutions.
Integrate data from APIs, databases, applications, files, and other systems.
Implement data validation, quality checks, monitoring, and reconciliation.
Optimise pipelines and queries for performance, scalability, and cost.
Implement access controls, encryption, audit logging, and data protection.
Support data governance, classification, lineage, metadata, retention, and lifecycle management.
Develop data solutions using cloud and/or on-premises technologies.
Work with Python, SQL, Spark, Airflow, Kafka, Databricks, Snowflake, AWS, Azure, or Google Cloud.
Implement CI/CD, automation, testing, and deployment practices.
Monitor production pipelines, troubleshoot issues, perform root-cause analysis, and support service restoration.
Collaborate with stakeholders to translate data requirements into technical solutions.
Support disaster recovery, business continuity, and technology resilience activities.

Requirements

Degree/Diploma in Computer Science, IT, Data Engineering, Engineering, Mathematics, Statistics, or related discipline.
3–6 years of relevant experience in data engineering, data platforms, ETL/ELT, or related technical roles.
Strong programming skills in Python, Java, Scala, or similar.
Strong SQL and relational database experience.
Hands-on experience designing and implementing data pipelines and integration solutions.
Experience with data warehouses, data lakes, modern data platforms, and cloud technologies.
Understanding of data security, governance, and data quality.

Good to Have

Experience with Spark, Databricks, Airflow, Kafka, dbt, or Snowflake.
Experience with AWS, Azure, or Google Cloud.
Experience with CI/CD, DevOps, data modelling, metadata, data lineage, or data cataloguing.