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Staff Data Engineer at Sparta
At Sparta, we're on a mission to build the next generation of commodity trading platforms – replacing the fragmented tools traders depend on with a single, powerful dashboard. Our product is a data-driven platform that aggregates real-time feeds from across the commodities domain, transforming them into intuitive, actionable visualisations within one unified interface. Users can explore pre-built data views or build their own custom formulas—turning raw market data into decisions.
At the heart of Sparta lies a serious data engineering challenge. We're building a platform that enables the rapid development of reliable, scalable ETL pipelines—the foundation everything else depends on. As our data volumes grow and our product ambitions expand, the demands on that infrastructure grow with them. We're looking for experienced data engineers who understand what it takes to keep a data platform performing as complexity increases.
We want people who thrive with ownership. At Sparta, you'll be handed problems—not solutions—and we expect you to run with them. Speed matters, autonomy is real, and we’d rather you move fast and course-correct than wait for permission. As a Staff engineer, you’ll also be shaping the direction of the platform itself—engaging directly with stakeholders, contributing to product thinking, and investing meaningfully in the engineers around you.
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This role can be based in the UK (London) or Spain (Barcelona/Madrid), with an expectation to spend 2–3 days a week in-office together with the team.
Core Responsibilities
- Play a leading role in the design, build, and ongoing evolution of scalable data pipelines and ETL frameworks that power both real-time and analytical data processing.
- Take shared ownership of the health and performance of our data platform, optimising for latency, throughput, and reliability at scale.
- Collaborate closely with engineering and cross-functional stakeholders to ensure data infrastructure aligns with product goals and trader-facing outcomes—acting as the connective tissue between data and backend engineering.
- Drive architectural decisions across our data platform, with a focus on pipeline reliability, data quality, and scalability.
- Contribute to the definition of our target architecture, shaping how we ingest, transform, store, and serve data as the platform evolves.
- Mentor and support other engineers through design reviews, technical discussions, and hands-on knowledge sharing.
Skills & Experience
Required:
- 7+ years of experience as a data or software engineer, with a strong track record of delivering production-quality data systems.
- 2+ years working within a product-focused organisation, collaborating closely with cross-functional teams.
- Proven experience building and scaling data-intensive pipelines and platforms in production environments.
- Deep understanding of stream and batch processing frameworks—hands-on experience with Apache Flink or Spark is essential.
- Comfortable working across multiple programming languages—we primarily use Kotlin, Python, and TypeScript.
- Equally capable of designing high-level architecture as well as diving into low-level implementation details.
- Experience designing and deploying data infrastructure in cloud environments such as AWS or GCP.
- Hands-on experience with core data infrastructure tooling, including Kafka, Redis, and/or clustered PostgreSQL.
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Nice to have:
- Familiarity with open lakehouse technologies, such as Apache Iceberg and dbt.
- Experience building data infrastructure to support AI agents—including low-latency retrieval and analytical interfaces.
- Experience leading teams or managing engineers, driving both delivery and technical excellence.
- Exposure to complex distributed environments with demanding constraints such as high throughput, low latency, or large-scale datasets.
Working Style
Whereas Sparta is a remote-first company, for this role we’re seeking someone who prefers a hybrid working style with:
- A typical week involving 2–3 days in the office, with built-in flexibility.
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