Lead Data Engineer

Highbury
1 week ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer - Microsoft Fabric

Global Data Engineering Lead, Data Engineer

Lead Data Engineer
Salary/Rate: £100,000 - £110,000 per annum + Bonus
Location: North London
Company: Retelligence
About Retelligence
Retelligence is partnering with a high-growth, forward-thinking organization that specializes in digital innovation and marketing across international markets. The company is on an exciting journey, rapidly scaling its capabilities and leveraging advanced technology to deliver cutting-edge solutions. Join a dynamic team within a business that values innovation, supports professional development, and offers exceptional career progression.
The Role
Retelligence is seeking a Lead Data Engineer to take a hands-on role in designing and delivering robust, real-time data pipelines and infrastructure in a Google Cloud Platform (GCP) environment. The company is particularly interested in candidates with strong expertise in SQL. As the Lead Data Engineer, you’ll play a critical role in shaping their data architecture and driving transformation. You’ll partner closely with engineering, product, and analytics teams to ensure efficient, high-performance data systems that enable the business to thrive in a fast-paced environment.
Key Responsibilities:


  • Design, develop, and maintain scalable, data pipelines and infrastructure in a GCP environment.

  • Integrate multiple data sources to ensure seamless data flow across the organization.

  • Build and optimize data models for querying and analytics use cases.

  • Develop fault-tolerant, highly available data ingestion and processing pipelines.

  • Continuously monitor and improve pipeline performance for low-latency and high-throughput operations.

  • Ensure data quality, integrity, and security across all systems.

  • Implement effective monitoring, logging, and alerting mechanisms.

  • Collaborate with product, engineering, and analytics teams to deliver tailored solutions that meet business needs.

About You


  • Strong hands-on experience in data engineering with expertise in Python.

  • Proven track record of building and managing data pipelines.

  • In-depth experience with Google Cloud Platform (GCP) and its associated tools for data ingestion and processing.

  • Familiarity with distributed streaming platforms such as Kafka or similar technologies.

  • Advanced knowledge of SQL.

  • Experience with data orchestration tools.

  • Ability to optimize and refactor data pipelines for improved performance and scalability.

  • Strong problem-solving skills and the ability to thrive in a collaborative, fast-paced environment

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.

Careers

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Careers

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Careers

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.

🍪 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.