Senior Data Engineer

Blis
Edinburgh
2 days ago
Create job alert

Come work on fantastically high-scale systems with us! Blis is an award-winning, global leader and technology innovator in big data analytics and advertising. We help brands such as McDonald's, Samsung, and Mercedes Benz to understand and effectively reach their best audiences.


We are looking for solid and experienced Data Engineers to work on building out secure, automated, scalable pipelines on GCP. We receive over 350gb of data an hour and respond to 400,000 decision requests each second, with petabytes of analytical data to work with.


We tackle challenges across almost every major discipline of data science, including classification, clustering, optimisation, and data mining. You will be responsible for building stable production level pipelines maximising the efficiency of cloud compute to ensure that data is properly enabled for operational and scientific cause.


This is a growing team with big responsibilities and exciting challenges ahead of it, as we look to reach the next 10x level of scale and intelligence.


At Blis, Data Engineers are a combination of software engineers, cloud engineers, and data processing engineers. They actively design and build production pipeline code, typically in Python, whilst having practical experience in ensuring, policing, and measuring for good data governance, quality, and efficient consumption. To run an efficient landscape we are ideally looking for candidates that are comfortable with event-driven automation across also aspects of our operational pipelines.


As a Blis data engineer, we seek to understand the data and problem definition and find efficient solutions, so critical thinking is a key component to efficient pipelines and effective reuse, this must include defining the pipelines for the correct controls and recovery points not only function and scale. The team are almost always adherents of Lean Development and work well in environments with significant amounts of freedom and ambitious goals.


Key responsibilities

  • Design, build, monitor, and support large scale data processing pipelines.
  • Support, mentor, and pair with other members of the team to advance our team’s capabilities and capacity.
  • Help Blis explore and exploit new data streams to innovative and support commercial and technical growth
  • Work closely with Product and be comfortable with taking, making and delivering against fast paced decisions to delight our customers.

This ideal candidate will be comfortable with fast feature delivery with a robust engineered follow up.


Skills And Requirements

  • 5+ years direct experience delivering robust performant data pipelines within the constraints of direct SLA’s and commercial financial footprints.
  • Proven experience in architecting, developing, and maintaining Apache Druid and Imply platforms, with a focus on DevOps practices and large-scale system re-architecture
  • Mastery of building Pipelines in GCP maximising the use of native and native supporting technologies e.g. Apache Airflow
  • Mastery of Python for data and computational tasks with fluency in data cleansing, validation and composition techniques.
  • Hands-on implementation and architectural familiarity with all forms of data sourcing i.e streaming data, relational and non-relational databases, and distributed processing technologies (e.g. Spark)
  • Fluency with all appropriate python libraries typical of data science e.g. pandas, scikit-learn, scipy, numpy, MLlib and/or other machine learning and statistical libraries
  • Advanced knowledge of cloud based services specifically GCP
  • Excellent working understanding of server-side Linux
  • Professional in managing and updating on tasks ensuring appropriate levels of documentation, testing and assurance around their solutions.

Desired

  • Experience optimizing both code and config in Spark, Hive, or similar tools
  • Practical experience working with relational databases, including advanced operations such as partitioning and indexing
  • Knowledge and experience with tools like AWS Athena or Google BigQuery to solve data-centric problems
  • Understanding and ability to innovate, apply, and optimize complex algorithms and statistical techniques to large data structures

Experience with Python Notebooks, such as Jupyter, Zeppelin, or Google Datalab to analyze, prototype, and visualize data and algorithmic output.


About Us

Blis is the only omnichannel DSP that unites telco data, real-world movement patterns, and transactions to deliver a complete view of the consumer. Powered by T-Mobile and built for precision at scale, Blis' omnichannel platform helps marketers map the full purchase journey – from impression to transaction – and expand their audience reach, driving incremental results across every screen.


Founded in the UK in 2004, Blis employs over 300 global employees across 14 offices in 11 countries.


Our Values - B.L.I.S.

Brave – We're leaders not followers. An innovation and growth mindset helps us solve everyday challenges and achieve breakthroughs. Our passion drives us to innovate. We don’t see barriers, just possibilities. We take ownership and hold ourselves accountable for outcomes, good and bad – and we don’t pass the buck.


Love our clients – We're client obsessed. We do what we say and build trusted relationships with our partners for the long term. We act with integrity. We put our clients at the center of our business. We obsess over the best insights, ideas and solutions to deliver WOW and work with honesty and accountability to get it done.


Inclusive – We're one team. We are empathetic and embrace diversity. Everyone has a voice and can bring their authentic self to work. We care about and support each other – with humility and good humor. Mutual respect and wellbeing are key. We strive to eliminate bias and be open and transparent.


Solutions driven – We're action oriented. Speed matters in business, so we're solution-driven and action-oriented. We value simplification and calculated risk taking. We are lean, agile and resourceful self-starters. We collaborate and break silos, working thoughtfully and with urgency to solve problems, while learning from mistakes and celebrating wins.


If this looks like the perfect fit for you or if you just want to have a conversation, please apply and we will get back to you as quickly as possible!


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

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 Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.