Data Engineer

Responsibilities

• Design, build, and maintain scalable and reliable data pipelines and ETL/ELT workflows to support analytics, reporting, and machine learning use cases.
• Develop and optimize large-scale data models, schemas, and data warehouse architectures for performance and cost efficiency.
• Partner with Data Scientists, Product Managers, and Software Engineers to understand data requirements and deliver robust data solutions.
• Implement data quality frameworks including monitoring, validation, alerting, and anomaly detection to ensure data integrity and reliability.
• Evaluate and adopt best practices for data governance, privacy compliance, and data lifecycle management.

Minimum Qualifications

• 5+ years of hands-on experience in data engineering, data platform development, or a related technical role.
• Expert proficiency in SQL and experience working with large-scale data warehouses (e.g., Hive, Spark, Presto).
• Strong programming skills in Python or Java for building data pipelines and automation.
• Proven experience designing and operating production-grade ETL/ELT pipelines with workflow orchestration tools.
• Deep understanding of data modeling concepts, including dimensional modeling, star/snowflake schemas, and data vault methodologies.
• Experience with distributed computing frameworks (e.g., Spark, MapReduce) and large-scale data processing.
• Strong understanding of data quality practices, data governance, and privacy compliance requirements.
• Demonstrated ability to independently drive complex, ambiguous projects from inception to delivery.
• Excellent communication skills with the ability to articulate technical concepts to both technical and non-technical stakeholders.
• AI influence in cloud.

Must-Have Skills

1. Strong technical skills in expert SQL (pipeline-level optimisation, not basic queries), advanced Python (for orchestration and pipeline building, not just analysis), and Spark for large-scale data processing. You will be expected to build data pipelines from scratch and optimise existing data pipelines.
2. Experience using AI tools for pipeline building, logging, and dashboard development (building with React using AI coding tools).

Ability to work independently with cross-functional teams (Product Managers, Data Scientists, and Engineers) — self-driven, proactive, and doesn't need to be told what to work on.