Data Engineering Manager

NewDay
London, United Kingdom
3 months ago
Applications closed

Related Jobs

View all jobs

Specialist Solutions Architect - DE/DWH

Databricks London, United Kingdom

Engineering Manager - Storage

Databricks London, United Kingdom

Data Warehouse Manager

Reed Technology Bradford, United Kingdom

Data Governance & Enablement Manager

Orbit Group Binley Woods, United Kingdom

Software Delivery Manager

The Portfolio Group London, City And County Of the City Of London, United Kingdom
£80,000 pa

Data Platform Solutions Architect (Professional Services)

Databricks London, United Kingdom
£40,000 – £80,000 pa Hybrid
Posted
20 Jan 2026 (3 months ago)
Share
Save job
Create job alert

Join to apply for the Data Engineering Manager role at NewDay

Permanent

What You Will Be Doing
  • Support Head of Data Engineering and contribute to establish / enforce best practices, coding standards, and development methodologies for the data engineering team. Drive continuous improvement in data engineering processes to enhance efficiency, reliability and scalability.
  • Manage a team of skilled data engineers, providing guidance, mentorship, and support to ensure the team's success in delivering high-quality solutions.
  • Contribute to defining and executing the data engineering roadmap, collaborating with other technical and business leaders to align data initiatives with overall business goals.
  • Possess and maintain a deep understanding of the technical stack, including Scala, Python, SQL, Snowflake, and DBT. Provide technical guidance and expertise to the team when facing complex challenges.
  • Collaborate with data architects and analysts to design and optimise data models, pipelines, and workflows that support data transformation, integration, and analysis.
  • Ensure data accuracy, consistency, and reliability throughout the data pipelines. Implement monitoring, alerting, and quality control mechanisms to identify and address issues proactively.
  • Work closely with business teams, analysts and data science to understand their requirements and provide timely data solutions that support their objectives.
  • Plan, prioritise, and oversee multiple data engineering projects simultaneously, ensuring on-time delivery and high-quality outcomes.
Your Skills And Experience
  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
  • Proven experience (9+ years) in data engineering with a focus on building scalable data pipelines, data integration, and data transformation.
  • Demonstrated experience (3+ years) in managing and leading data engineering teams.
  • Strong proficiency in Scala, Python, SQL, Snowflake and DBT.
  • In-depth understanding of data modelling, ETL processes, and data warehousing concepts.
  • Experience with cloud-based data platforms (e.g., AWS, Azure, GCP) and containerisation technologies (e.g., Docker, Kubernetes) is a plus.
  • Excellent problem-solving skills and a track record of delivering innovative and practical data solutions.
  • Strong communication skills with the ability to collaborate effectively across technical and non-technical teams.
  • Detail-oriented mindset with a focus on data accuracy and quality.
Seniority level
  • Mid-Senior level
Employment type
  • Full-time
Job function
  • Engineering and Information Technology

Referrals increase your chances of interviewing at NewDay by 2x.

We work with Textio to make our job design and hiring inclusive.


#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.

Products

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

Jobs

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

Careers

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.

🍪 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.