Data Engineering Manager

NewDay
London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Data Engineering Manager

Data Engineering Manager

Data Engineering Manager

Data Engineering Manager

Data Engineering Manager

Engineering Manager - Data Quality & Governance

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

We need knowledge, experience + expertise in:

  • 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

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

Permanent

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.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.