Principal Data Engineer

albelli
London
1 month ago
Create job alert
About Our Data & ML Team

Data & AI powers our growth and innovation. We build enterprise platforms to create actionable insight enabling colleagues to make effective decisions and create proactive, automated, hyper-personalised experiences for our customers.

We have a growing data and AI team in the UK and Netherlands. We function as the backbone for a variety of data-hungry consumers and platforms across the business within Marketing, Finance, Operations and Product teams. Our AI photo services are at the heart of our consumer experience and we’re expanding our footprint towards decentralised ML adoption within the business.

As a team, we have come together through mergers. We are at the end of a phase of simplification of legacy infrastructure and moving into the next phase of consolidation and growth.

Who We’re Looking For

We are looking for an experienced Principal Data Engineer to join our London-based data team and help us (re-)build a platform that accelerates a decentralised-by-design data adoption model within the business. We care about providing trustable, usable data that meets business needs. We want you to own and drive technical excellence within your team to make this organisation-level change happen.

You’re a software engineering technical leader at heart. You thrive in the data engineering and analytics engineering domain. You care deeply about customer outcome-focused data engineering excellence.

Areas of ExpertiseTechnical Leadership
  • Extensive experience in data engineering, including designing, building, and maintaining robust and scalable data platforms that enable key business initiatives
  • Proven ability to lead technical design and architecture discussions, influencing technical strategy and decisions across the organization
  • Experience mentoring and coaching junior engineers, fostering a culture of technical excellence within the team
Data Engineering Expertise
  • Deep understanding of data engineering principles and best practices, including data modeling, observable ETL/ELT processes, data warehousing, and data governance
  • Proficiency in data manipulation languages (e.g., SQL/DBT) and programming languages relevant to data engineering (e.g., Python)
  • Experience with a variety of data processing frameworks and technologies, including cloud-based data services
Software Engineering Practices
  • Experience with software development lifecycle (SDLC) best practices, including version control (e.g., Git), testing, and continuous integration/continuous delivery (CI/CD)
  • A focus on building high-quality, maintainable, and well-documented code
Collaboration and Communication
  • Exceptional communication and collaboration skills, with the ability to effectively communicate technical concepts to both technical and non-technical audiences
  • Proven ability to work effectively in a cross-functional team environment, collaborating with product managers, analysts, and other stakeholders
  • Proven effective influencer, capable of driving change outside of your direct team, contributing to the wider engineering community
Education
  • A degree in a STEM field (e.g., Computer Science, Software Engineering, Mathematics) or equivalent practical experience
Your Daily Adventure at Storio
  • Collaborate effectively within a cross-functional, mission-led team, led by a product manager and engineering manager, contributing to the team’s strategy, roadmap, and OKRs
  • Define and champion technical principles and practices to raise the bar on implementing a well-engineered, well-governed data platform that meets the needs of our customers
  • Be accountable for the technical health of your team’s codebase, driving continuous improvement and establishing metrics to track progress
  • Lead technical solution design on multiple complex initiatives within your team, demonstrated by successful and timely implementation, driving resolutions for complex and difficult problems
  • Drive continuous improvement on key metrics such as business value, cost efficiency, speed, and quality of delivery
  • Coach a team of data and analytics engineers on best practices in the software development lifecycle, delivering high-quality software through your own work, and fostering a feedback culture within the team
  • Influence key decision-making across the data and ML engineering domain on technical approaches to balance delivering near-term commercial impact and building long-term foundations
Our Tech Stack
  • Cloud Data Warehouse - Snowflake
  • AWS Data Solutions - Kinesis, SNS, SQS, S3, ECS, Lambda
  • Data Governance & Quality - Collate & Monte Carlo
  • Infrastructure as Code - Terraform
  • Data Integration & Transformation - Python, DBT, Fivetran, Airflow
  • CI/CD - Github Actions / Jenkins
Nice To Have Experience
  • Understanding of various data architecture paradigms (e.g., Data Lakehouse, Data Warehouse, Data Mesh) and their applicability to different business needs
  • Experience with data governance principles and practices, ensuring data quality, accuracy, and compliance
  • Familiarity with data security best practices and technologies
  • Experience in the e-commerce domain
About Us

At Storio Group, we help people hold onto life’s moments. We make personalised photo products that turn fleeting memories into things you can keep, share, and re-live.

Every person at Storio Group helps create our products and shape our company. You will see the impact of your work daily. We invite you to make your mark on our business, products, and customers' lives.

We act with heart by putting people first and valuing diverse perspectives. We give our best and aim for high standards in all we do. We own our work, taking initiative to find solutions. We embrace curiosity, always learning and trying new things. We find the joy in our work and create a positive environment.


#J-18808-Ljbffr

Related Jobs

View all jobs

Principal Data Engineer

Principal Data Engineer (GCP)

Principal Data Engineer (MS Azure)

Principal Data Engineer

Principal Data Engineer

Principal 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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.