Data Engineering Manager

TalentHawk
portsmouth, yorkshire and the humber, uk
9 months ago
Applications closed

Related Jobs

View all jobs

Data Engineering Manager

BI and Data Engineering Lead

Digital Data Consultant, Data Engineering, Data Bricks, Part Remote

Lead Data Scientist

Senior Data Engineer

Data Analyst

The Data Engineering Manager is responsible for establishing and overseeing the Data Engineering and Data Ops functions, ensuring the efficient and effective management of data to drive business value.


Key Responsibilities

  • Develop and own the data engineering strategy and roadmap to maximize long-term business value.
  • Prioritize, plan, and ensure the timely and high-quality delivery of data engineering initiatives.
  • Oversee third-line support, technology upgrades, and the introduction of new technologies within agreed timelines.
  • Provide technical guidance and mentorship to the team and wider organization on data engineering challenges and solutions.
  • Design and architect scalable data pipelines for efficient data ingestion, transformation, and loading.
  • Manage and optimize data platforms, including infrastructure, upgrades, and connectivity.
  • Build and lead a high-performing Data Engineering team, including internal staff and third-party resources.
  • Establish clear service definitions, SLAs, and performance expectations for the team, ensuring adherence.
  • Act as a data and analytics champion, fostering a culture of innovation and excellence within the Analytics & Insight team.
  • Stay abreast of industry trends and emerging technologies to enhance data infrastructure and capabilities.
  • Manage budgets for data-related activities and projects within the broader analytics budget.
  • Establish and manage third-party commercial agreements, including vendor selection and contract negotiations.
  • Collaborate with stakeholders across functions to align data engineering initiatives with business goals.
  • Leverage a deep understanding of the business and data landscape to drive value through data initiatives.


Required Expertise

  • Degree or equivalent qualification in a data-related discipline or relevant experience in high-performing Data Engineering and Analytics functions.
  • Proven leadership experience in managing Data, Environment, and Release Delivery teams, including resource and cost management.
  • Expertise in Data Engineering and Environment management, preferably in AWS, with experience in automation tools.
  • Strong knowledge of SQL & Python, with hands-on experience in data engineering tools and technologies.
  • Experience working on data science and machine learning projects.
  • Familiarity with Data Ops or DevOps environments and software development life cycles.


Key Competencies & Attributes

  • Strong team development and performance management skills.
  • Ability to coach and motivate teams under pressure and manage competing priorities.
  • A commitment to continuous learning and staying up to date with evolving technologies.
  • Attention to detail, fairness, and integrity.
  • Inquisitive and innovative mindset, with a drive to explore new processes and methodologies.
  • Excellent communication and collaboration skills, with the ability to engage stakeholders across business functions.
  • A positive leader with a growth mindset, striving to build a high-performing data function.
  • Strong decision-making and problem-solving capabilities.
  • Ability to balance business objectives with resource constraints and competing priorities.

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 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.

Jobs

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.

Careers Jobs

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.

🍪 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.