Engineering Manager (Data)

Storio group
London
2 months ago
Applications closed

Related Jobs

View all jobs

Data Engineering Manager

Data Engineering Manager

Data Engineer Manager

Head of Data Engineering - Product & Plan for Better

Head of Data Engineering - Product & Plan for Better (Basé à London)

Head of Data Engineering - Product & Plan for Better

Get AI-powered advice on this job and more exclusive features.

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.

About The Role
We are looking for an experienced Engineering Manager 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 create and lead a high-performing team that delivers on this mission.
Whilst this is not a role that requires hands-on software development, we are looking for an experienced manager who has a strong delivery and technical background. You’ve been a software or data engineer in the past and now thrive in building and connecting technical teams to business outcomes.

Your Daily Adventure at Storio

Primary Responsibilities Include

People Management:

  • Lead a cross-functional mission-led team of 5-8 data engineers and analytics of mixed seniority.
  • Coach individuals to grow their career, increase mastery and autonomy whilst holding them accountable for high performance.
  • Own the recruitment process for your team.
  • Accountable for team health, fostering an open and collaborative feedback culture.

Delivery/Execution:

  • Partner with a Product Manager to define initiatives aligned with our OKRs and strategy, scope projects and define milestones.
  • Communicate with key stakeholders at all levels.
  • Accountable for delivery performance of the team.
  • Accountable for team processes and ways of working.
  • Drive continuous improvement on key metrics such as business value, cost efficiency, speed, and quality of delivery.
  • Own resolution of cross-team dependencies.

Technical Excellence:

  • Oversee design decisions owned by the principal engineer of the team and domain.
  • Cultivate best engineering practices within the software development lifecycle.
  • Balance tech health and quality excellence with time to delivery.
  • Create opportunities for technical exploration and innovation within your team.

Cross-team Leadership:

  • Influence decision-making across the technical organisation as part of a wider community of engineering managers and technical leaders.
  • Establish partnerships within and outside the domain to amplify the impact of your team.
  • Lead in-domain standardisation efforts on people, process and technology.
  • Own your team’s people and tech budgets in relation to the value created by the team within the business.

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

What You Bring To The Party:

  • Solid track record of building and leading high-performance data and analytics engineering teams.
  • Experience in navigating a complex enterprise-wide customer landscape with competing priorities.
  • Experience in building platforms-as-a-product through agile delivery methods and in lockstep with product managers, senior engineers and data governance leads.
  • Experience guiding teams to make significant technical data and analytics engineering decisions spanning source data ingest to consumption.
  • Excellent communication skills, making complex topics simple, transparent and easy to act upon.
  • Prior experience as a senior software or data engineer.

Extra Kudos For Experience:

  • A degree in a STEM field, e.g. Computer Science, Software Engineering, Mathematics.
  • Knowledge of the ecommerce domain.
  • Understanding of data architecture paradigms and their applicability to business needs.

Sounds exciting?Apply now with your resume and cover letter!

Seniority level

  • Mid-Senior level

Employment type

  • Full-time

Job function

  • Engineering and Information Technology

Industries

  • Software Development

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.