Kpler3mo ago

Senior Backend Engineer

London,LondonFull-timesenior

EngineeringOtherBackend EngineeringSenior Backend Engineer

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Quick Summary

Overview

At Kpler, we are dedicated to helping our clients navigate complex markets with ease. By simplifying global trade information and providing valuable insights, we empower organisations to make informed decisions in commodities, energy, and maritime sectors.

Technical Tools

elasticsearchkafkakubernetesci-cddatabase-designetlmicroservicesstreaming-data

Since our founding in 2014, we have focused on delivering top-tier intelligence through user-friendly platforms. Our team of over 700 experts from 35+ countries works tirelessly to transform intricate data into actionable strategies, ensuring our clients stay ahead in a dynamic market landscape. Join us to leverage cutting-edge innovation for impactful results and experience unparalleled support on your journey to success.

You will join a data-intensive product team responsible for building and operating core data pipelines and backend systems that model and deliver insights on global refinery operations and economics. The team works at the intersection of data engineering, backend engineering, and data science, transforming diverse industrial and market data into reliable, production-grade datasets used by customers and internal teams.

This role is a senior individual contributor position, with significant ownership over data architecture, pipeline reliability, and engineering standards. You will help shape how data is engineered, validated, and delivered across the Refineries product.

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.

Continuously improve existing systems as data volume, complexity, and product expectations grow.

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.

Continuously improve existing systems as data volume, complexity, and product expectations grow.

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.

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, without being a primary frontend contributor).

Prior experience working on data products in energy, commodities, or industrial domains.