AI & Data Engineer
Summary
Designs, builds, and maintains scalable data pipelines and automated ETL/ELT workflows on cloud platforms (AWS services like S3, Glue, Redshift, plus Snowflake and Databricks), supporting analytics and AI/ML initiatives. Requires strong Python engineering, data governance, and CI/CD/IaC practices.
Responsibilities
. Design, develop, and maintain scalable data pipelines for ingestion,transformation, and delivery.
. Build and automate ETL/ELT workflows to improve efficiency, reliability,and scalability.
. Develop data solutions using AWS services such as S3, Glue, Redshift,EMR, Athena, and Lambda.
. Work with Data Scientists and business stakeholders to support analytics,reporting, and AI/ML initiatives.
. Implement monitoring, logging, and data quality processes to ensurereliable data delivery.
. Maintain data governance, security, and compliance with organisationalpolicies and relevant regulations.
. Contribute to CI/CD, Infrastructure-as-Code, and automation initiativesto improve engineering practices.
. Document data pipelines, architecture, and technical processes.
Requirements
. Minimium 5 years of experience in Data Engineering and modern cloud dataplatforms.
. Strong expertise across AWS, Azure, and/or GCP ecosystems.
. Extensive experience with Snowflake and Databricks.
. Strong Python engineering skills and software engineering fundamentals.
. Experience leading technical delivery workstreams and mentoringengineers.
. Experience designing and implementing AI and ML data platforms.
. Experience implementing model monitoring and observability capabilities.
. Strong stakeholder management and communication skills.
. Ability to align technical solutions with business outcomes.