Data Engineer

ParleyX
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

ABOUT THE COMPANY

A fast-growing fintech company is hiring a Data Engineer to support the next phase of its product and data infrastructure evolution. The company is on a mission to make investing simple, accessible, and cost-effective - removing long-standing barriers and enabling millions of individuals to grow their wealth with confidence. In early 2025, it announced a strategic acquisition by a leading global investment group. The business remains independent, while gaining access to scale, resources, and market reach.

ABOUT THE ROLE

The Data Engineer will join a growing data team focused on transforming high-volume data into trusted insights, tooling, and infrastructure. This role involves hands-on pipeline development, cloud deployment, and collaboration across product and engineering. You\'ll work in a modern data stack, help scale internal analytics capabilities, and shape the data foundations of a next-gen investment platform.

KEY RESPONSIBILITIES
  • Design, build, and deploy data pipelines using Python and SQL
  • Orchestrate pipelines in Dagster and deploy jobs into a Kubernetes cluster
  • Model clean, reliable datasets within BigQuery
  • Improve data quality and monitoring through alerting and automated testing
  • Deploy data infrastructure via Terraform and maintain CICD pipelines
  • Enhance operational efficiency and reliability of the data platform
  • Collaborate with product and engineering stakeholders to define best practices and deliver data-driven solutions
REQUIREMENTS
  • Proven experience writing and maintaining data pipelines in production
  • Strong coding skills in Python and SQL, with attention to testing and maintainability
  • Experience deploying data-centric applications in cloud environments (Google Cloud preferred)
  • Familiarity with a range of data sources including relational DBs, NoSQL, APIs, and cloud storage
  • Understanding of data security, privacy, and protection principles
  • Comfortable owning regular processing jobs and responding to data issues
WORKING MODEL
  • Hybrid model: 3 days in the London office (Monday, Tuesday, Thursday), 2 days remote
  • Designed for deep in-person collaboration with flexibility for personal circumstances
  • Supportive of parents and those with caregiving responsibilities
BENEFITS & CULTURE
  • Competitive salary with structured benchmarking
  • 25 days annual leave plus UK public holidays, birthday off, and tenure-based bonus days
  • Enhanced pension with up to 5% company match
  • Private health insurance including mental health, dental, and vision care
  • Group life insurance at 5x salary and income protection cover
  • Enhanced parental leave for all caregiver types
  • Learning & development budget including sponsorship for industry qualifications
  • Cycle-to-work scheme with tax savings
  • Paid sick leave (10 days annually)
  • Values-led culture built on honesty, focus, and grit-expect these to guide your interview process

If you\'re a data engineer looking to build at scale, collaborate with smart cross-functional teams, and shape data infrastructure at a growing fintech, this opportunity offers both impact and career growth.


#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.

Careers

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

Jobs

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

Education

The Skills Gap in Data Engineering Jobs: What Universities Aren’t Teaching

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.