Data Engineer

Ripjar
Manchester
1 day ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

About Ripjar

Ripjar is a UK based software company that uses data and machine learning technologies to help companies and governments prevent financial crimes and terrorism. For example, our software was helping many financial institutions and corporations comply with sanctions on Russian entities. Ripjar originally span out from GCHQ and now has 130 staff based in Cheltenham and remotely and are beginning to expand globally. We have two successful, inter-related products; Labyrinth Screening and Labyrinth Intelligence. Labyrinth Screening allows companies to monitor their customers or suppliers for entities that they aren\u2019t allowed to or do not want to do business with (for ethical or environmental reasons). Labyrinth Intelligence empowers organisations to perform deep investigations into varied datasets to find interesting patterns and relationships. Data infuses everything Ripjar does. We work with a wide variety of datasets of all scales, including an always-growing archive of 8 billion news articles, sanctions and watchlist data, 250 million organisations and ownership data from global corporate registries.


The role

Ripjar has several engineering teams that are responsible for the processing infrastructure and many of the analytics that collect, organise, enrich and distribute this data. Central to almost all of Ripjar\u2019s systems is the Data Collection Hub, which captures data from various sources, processes and analyses it, and then forwards it on to multiple end-user applications. The system is developed and maintained by 3 teams of software engineers, data engineers, and data scientists. We are looking for an individual with at least 2 years industrial or commercial experience in data processing systems to come in and add to this team. Ripjar values engineers who are thoughtful and thorough problem solvers who are able to learn new technologies, ideas and paradigms quickly.


Responsibilities
  • Contributing production quality code and unit-tests to our Data Collection Hub
  • Contributing improvements to the test and build pipelines
  • Considering the impact and implications of changes and communicating these clearly
  • Helping to support the data processing pipelines as needed
  • Modelling data in the best way for specific business needs
  • Staying abreast of the latest developments in Data Engineering to contribute to Ripjar\u2019s best practices
  • Adding to Ripjar\u2019s culture and make it a fun and rewarding place to work!

Requirements
  • You will be using Python (specifically pyspark) and Node.js for processing data
  • You will be using Hadoop stack technologies such as HDFS and HBase
  • Experience using MongoDB and Elasticsearch for indexing smaller datasets would be beneficial
  • Experience using Airflow to co-ordinate the processing of data would be beneficial
  • You will be using Ansible to manage configuration and deployments

Salary and benefits
  • Salary DOE
  • 25 days annual leave + your birthday off, in addition to bank holidays, rising to 30 days after 5 years of service
  • Remote working
  • Private Family Healthcare
  • Employee Assistance Programme
  • Company contributions to your pension
  • Pension salary sacrifice
  • Enhanced maternity/paternity pay
  • The latest tech including a top of the range MacBook Pro


#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.