Opus Recruitment Solutions | Head of Data Engineering

Opus Recruitment Solutions
London
2 months ago
Applications closed

Related Jobs

View all jobs

Job Title: Head of Data Engineering


If the following job requirements and experience match your skills, please ensure you apply promptly.

Location:LondonType:Full-TimeSalary:Competitive

Our client in the Environmental sector is currently is seeking a Head of Data Engineering to lead their data engineering team and shape the organization’s data infrastructure. You will design and implement scalable data architecture, drive data governance, and ensure seamless data integration. Collaborating with key stakeholders like the CDO, CTO, and Head of Analytics, you'll play a crucial role in building data-driven strategies and supporting data science capabilities.

Job Description:We are seeking an experienced and visionary Head of Data to lead our data team. In this role, you will be responsible for the strategic direction, management, and execution of our data initiatives. You will play a pivotal role in shaping our data infrastructure, ensuring data quality, and enabling data-driven decision-making across the organization.

Key Responsibilities:

  • Strategy and Leadership:Develop and implement a comprehensive data strategy that aligns with our business objectives. Lead and mentor a team of data engineers, analysts, and scientists.
  • Data Infrastructure:Design, build, and maintain robust data infrastructure and pipelines to support data collection, storage, and analysis.
  • Data Governance:Establish and enforce data governance policies to ensure data quality, integrity, and security.
  • Collaboration:Work closely with cross-functional teams, including product, engineering, and business units, to understand their data needs and provide actionable insights.
  • Innovation:Stay up-to-date with the latest data technologies and industry trends. Drive innovation in data practices and tools to keep us ahead of the curve.
  • Performance Measurement:Define key performance indicators (KPIs) for data initiatives and monitor progress to ensure continuous improvement and impact.

Qualifications:

  • Experience:Proven experience in a senior data leadership role, with a track record of managing and scaling data teams.
  • Technical Skills:Expertise in data engineering, big data technologies (e.g., Hadoop, Spark), and cloud services (e.g., AWS, Google Cloud).
  • Analytical Skills:Strong analytical mindset with the ability to translate complex data into actionable insights.
  • Leadership:Exceptional leadership and team management skills. Ability to inspire and motivate a team to achieve their best.
  • Communication:Excellent communication and collaboration skills, with the ability to work effectively with both technical and non-technical stakeholders.
  • Education:Advanced degree in Computer Science, Data Science, Engineering, or a related field is preferred.

Benefits include...

  • Competitive salary and stock options.
  • 25 days holiday
  • Health insurance
  • Enhanced maternity and parental leave.
  • Generous pension (includes both employee and employer contributions).
  • Flexible working options
  • Personal training and development budget
  • Paid volunteering days.

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.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.