Senior Data Platform Engineer, ISS - Data Platform Engineering

THE ROLE

We’re building a new capability inside Platform Engineering, within our Infrastructure Shared Services (ISS) organisation: Data Platform Engineering. Our goal is to create a platform that removes friction, providing a consistent way to build, operate, and evolve data-driven services with clear ownership, guardrails, and a strong developer experience. Everpure’s own platform thinking is anchored in the idea of a unified data plane - a single fabric that connects data across locations and delivers consistent, secure data services across protocols and applications.

In this role, you’ll build and operate a trusted, scalable, and intelligent data platform that empowers teams to make faster data-driven decisions while shaping a culture of ownership and innovation to meet the needs of an ever-changing business and technology landscape.

WHAT YOU'LL DO

• Design, build, and operate the core data platform services that enable teams to ingest, model, query, and publish data seamlessly.
• Architect a multi-engine environment (e.g., Trino/Starburst, Dremio, ClickHouse, Postgres/pgLake) with an Iceberg-based lakehouse, prioritizing interoperability, operability, and security.
• Drive platform evolution through experimentation, measuring performance, reliability, and cost trade-offs to standardize high-impact solutions.
• Establish durable corporate knowledge by developing strong defaults, comprehensive documentation, and intuitive self-service interfaces.
• Manage core lakehouse components, including table layout conventions, catalogue integration, and critical lifecycle maintenance patterns such as compaction and optimization.
• Implement robust ingestion patterns for both batch and streaming data, ensuring clear SLAs and highly observable failure modes.
• Enable multi-engine access by configuring connectors and catalog permissions while maintaining strict guardrails for a consistent user experience.

WHAT YOU BRING

• 6+ years of experience in the design and development of data pipeline automation, specifically extracting data from API-based sources.
• Technical expertise in developing complex automation frameworks, data modeling, and ETL processes using SQL, Python, DBT, Apache Airflow, or similar languages.
• Full-stack proficiency across the data stack, with deep knowledge of Python, SQL, and ETL methodologies.
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