Data Engineer

We are seeking a hands-on Data Engineer with 5+ years of experience building scalable data pipelines, data warehouses, and cloud-based analytics platforms. The ideal candidate has strong SQL and Python skills, practical experience with ETL/ELT development, Apache Airflow orchestration, cloud data platforms, and enterprise data modelling.

Responsibilities:

• Build and maintain scalable ETL/ELT pipelines.
• Develop and optimise SQL transformations and warehouse queries.
• Create Airflow DAGs for orchestration and scheduling.
• Design source-to-target mappings and curated data layers.
• Support reporting, dashboards, and analytics use cases.
• Work with structured and semi-structured datasets.
• Monitor pipeline health, failures, and data quality.
• Collaborate with BI, analytics, and AI/ML teams.
• Contribute to cloud-based data platform development.
• Maintain technical documentation and data lineage.

The core requirements for the job include the following:

Core Skills and Experience:

• SQL: Advanced SQL development including joins, CTEs, window functions, optimisation, stored procedures, and data transformations.
• Python: Strong Python programming for ETL pipelines, automation, APIs, and data processing.
• ETL/ELT: Experience building robust ingestion and transformation pipelines from APIs, databases, flat files, and cloud sources.
• Apache Airflow: Hands-on experience designing DAGs, scheduling workflows, retries, monitoring, and dependency management.
• Cloud Platforms: Practical experience with AWS, Azure, or GCP data services.
• Data Modelling: Understanding of dimensional modelling, star schemas, fact/dimension tables, normalisation, and warehouse design.

Preferred Technical Stack:

• Languages: Python, SQL.
• Orchestration: Apache Airflow.
• Cloud Platforms: At least one of: Amazon Web Services, Microsoft Azure, Google Cloud Platform.
• Databases / Warehouses: Snowflake, BigQuery, Redshift, SQL Server, PostgreSQL.
• Data Processing: Pandas, PySpark (nice to have).
• Version Control: Git.

Good Indicators of a Strong Candidate:

• Has built production-grade pipelines end-to-end.
• Understands performance optimisation and debugging.
• Can independently write complex SQL.
• Comfortable working with cloud-native data services.
• Understands how data supports reporting and AI workflows.
• Has worked with enterprise-scale datasets.
• Demonstrates strong problem-solving and ownership.