Senior Data Engineer

TL;DR

Senior Data Engineer (Data Pipelines): Building and optimizing core data pipelines and backend systems for global refinery operations and economics with an accent on complex data ingestion, transformation, and validation workflows. Focus on driving improvements in pipeline reliability, scalability, and observability, and shaping data architecture and engineering standards.

Location: Hybrid in London

Company

hirify.global simplifies global trade information and provides valuable insights, empowering organizations in commodities, energy, and maritime sectors.

What you will do

• Design, build, and evolve batch and streaming data pipelines that power refinery modeling, analytics, and customer-facing products.
• Own complex data ingestion, transformation, validation, and delivery workflows across multiple data sources.
• Drive improvements in pipeline reliability, scalability, and observability, including retries, backfills, data quality checks, and monitoring.
• Lead schema design, versioning, and evolution strategies to support stable, long-lived data contracts.
• Build and maintain backend components and APIs used to serve data to downstream systems and applications.
• Partner closely with data scientists, product managers, and other engineers to translate domain requirements into robust technical solutions.

Requirements

• Deliver high-quality, well-tested, and maintainable code, setting a strong example for engineering best practices.
• Own significant parts of the data platform end-to-end, from ingestion to production delivery.
• Make architectural contributions to data processing, storage, and delivery patterns.
• Contribute to and improve CI/CD pipelines, automation, and operational tooling.
• Instrument services and pipelines with metrics, logs, and alerts, and help define operational standards.
• Play an active role in incident response, root-cause analysis, and long-term system improvements.
• Review code, mentor other engineers, and help reinforce shared coding and architectural standards across the team.

Nice to have

• Exposure to Kafka, Spark, or streaming architectures.
• Experience with Kubernetes.
• Familiarity with event-driven or microservices architectures.
• Exposure to analytical datastores (e.g., Elasticsearch).
• Full-stack awareness (e.g., ability to read, review, and provide feedback on frontend or API-layer pull requests).
• Prior experience working on data products in energy, commodities, or industrial domains.

Culture & Benefits

• A dynamic company dedicated to nurturing connections and innovating solutions to tackle market challenges.
• Committed to providing a fair, inclusive, and diverse work environment.
• Believes that different perspectives lead to better ideas.
• Acts decisively and with purpose, going the extra mile.
• Fosters relationships and develops creative solutions to address market challenges.
• Accessible and supportive to colleagues and clients with a friendly approach.