Data Engineer

Swap
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Swap is a leading software provider dedicated to empowering e-commerce brands with innovative, data-driven solutions. Our cutting-edge platform helps online retailers optimise their operations, enhance customer experiences, and drive growth. We are committed to fostering a collaborative and inclusive work environment where creativity and innovation thrive.


About the Role

We're looking for a passionate and detail oriented Data Engineer to join our platform team as we rapidly scale our stack for a new data-driven era at Swap. In this role, you’ll be driving change through building first-class data systems for the whole business whilst shaping the platform vision and driving internal best practices.


This role requires a knowledgeable, hands-on and enthusiastic team player who is excited at the prospect of accelerating and scaling a greenfield project. If you're excited by the idea of working in a fast-paced, collaborative team where you can make a real impact, we'd love to hear from you!


Responsibilities

  • API Strategy & Development: Own the strategy and implementation of our API layer, enabling our products to access the data they need.
  • Platform Technical Vision: Input and influence the technical direction of the data platform at Swap, ensuring we're always prepared for future business opportunities.
  • Data Pipeline Development: Own our replication pipelines to ensure we have first-class observability, latency and alerting capabilities.
  • Data Quality & Governance: Driving best practices to ensure our data is trustworthy and reliable as we scale together to deliver best in class products for Swap.

What We're Looking For

  • 5+ years experience working as a data engineer in customer-facing product environments.
  • Demonstrable Python experience working with data pipeline, orchestration, and api framework technologies. Ideally, but not limited to, Data Load Tool (dlt) and FastAPI.
  • Several years of experience working in a Google Cloud Platform environment, specifically BigQuery, Cloud Run and Cloud Storage.
  • Experience deploying different CI/CD technologies, such as GitHub Actions, to achieve scalable and seamless multi-developer projects.
  • Understanding of data warehouse architectures and best practices using data build tool (dbt).
  • Ability to clearly communicate and influence the technical vision and direction of the platform to technical and non-technical stakeholders.
  • Experience working with Shopify or e-commerce datasets.
  • Stock options in a high-growth startup
  • Private Health insurance
  • Pension


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Write a Data Engineering Job Ad That Attracts the Right People

Data engineering is the backbone of modern data-driven organisations. From analytics and machine learning to business intelligence and real-time platforms, data engineers build the pipelines, platforms and infrastructure that make data usable at scale. Yet many employers struggle to attract the right data engineering candidates. Job adverts often generate high application volumes, but few applicants have the practical skills needed to build and maintain production-grade data systems. At the same time, experienced data engineers skip over adverts that feel vague, unrealistic or misaligned with real-world data engineering work. In most cases, the issue is not a shortage of talent — it is the quality and clarity of the job advert. Data engineers are pragmatic, technically rigorous and highly selective. A poorly written job ad signals immature data practices and unclear expectations. A well-written one signals strong engineering culture and serious intent. This guide explains how to write a data engineering job ad that attracts the right people, improves applicant quality and positions your organisation as a credible data employer.

Maths for Data Engineering Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data engineering jobs in the UK, maths can feel like a vague requirement hiding behind phrases like “strong analytical skills”, “performance mindset” or “ability to reason about systems”. Most of the time, hiring managers are not looking for advanced theory. They want confidence with the handful of maths topics that show up in real pipelines: Rates, units & estimation (throughput, cost, latency, storage growth) Statistics for data quality & observability (distributions, percentiles, outliers, variance) Probability for streaming, sampling & approximate results (sketches like HyperLogLog++ & the logic behind false positives) Discrete maths for DAGs, partitioning & systems thinking (graphs, complexity, hashing) Optimisation intuition for SQL plans & Spark performance (joins, shuffles, partition strategy, “what is the bottleneck”) This article is written for UK job seekers targeting roles like Data Engineer, Analytics Engineer, Platform Data Engineer, Data Warehouse Engineer, Streaming Data Engineer or DataOps Engineer.

Neurodiversity in Data Engineering Careers: Turning Different Thinking into a Superpower

Every modern organisation runs on data – but without good data engineering, even the best dashboards & machine learning models are built on sand. Data engineers design the pipelines, platforms & tools that make data accurate, accessible & reliable. Those pipelines need people who can think in systems, spot patterns in messy logs, notice what others overlook & design elegant solutions to complex problems. That is exactly why data engineering can be such a strong fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & considering a data engineering career, you might have heard comments like “you’re too disorganised for engineering”, “too literal for stakeholder work” or “too distracted for complex systems”. In reality, the traits that can make traditional office environments hard often line up beautifully with data engineering work. This guide is written for data engineering job seekers in the UK. We’ll cover: What neurodiversity means in a data engineering context How ADHD, autism & dyslexia strengths map to common data engineering tasks Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data engineering – & how to turn “different thinking” into a genuine professional superpower.