Design and build scalable production‑grade data pipelines and infrastructure that directly support AI and ML systems across the Group.
Translate business use cases into clean, well‑architected technical solutions across both batch and streaming data environments.
Own data platform development across cloud environments, ensuring reliability, performance and scalability at enterprise scale.
Develop APIs, workflows and integrations that connect data infrastructure to AI and ML systems.
Lead delivery across small engineering pods, maintaining pace, quality and clear accountability.
Ensure all solutions meet production readiness standards, including monitoring, observability and fault tolerance.
About YouStrong hands‑on experience building and maintaining data pipelines using ETL and ELT patterns in production environments.
Proficiency with cloud data platforms such as Snowflake, Big Query or Redshift.
Experience with both batch and streaming processing frameworks such as Apache Spark, Kafka or equivalent.
Strong SQL and Python skills, with an understanding of software engineering principles applied to data.
Experience designing data architecture that supports AI and ML systems, including feature stores, data lakes and semantic layers.
Comfortable leading engineering teams and managing delivery across multiple work streams.
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