Senior Data Engineer

OFS
London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer - Microsoft Fabric

Senior Data Engineer - Microsoft Fabric

Senior Data Engineer – Permanent Role – £100,000 – £110,000 + Excellent Bonus & Benefits


A prestigious mid-sized Investment Management firm, specialising in ETFs, is seeking a Senior Data Engineer to join their dynamic team in London. This hybrid role offers a blend of remote working and office presence, providing flexibility and a balanced work environment.


Why This Role Stands Out


As a Senior Data Engineer, you will have the chance to shape the future of investment strategies with cutting-edge data solutions. This role not only offers a competitive salary and excellent bonus but also provides an environment where innovation and professional growth are highly valued. You will have the opportunity to mentor junior engineers, making a significant impact on their careers while advancing your own.


Your Role


In this pivotal position, you will support the Data Engineering Lead in constructing robust, scalable, and secure data systems. You will take ownership of critical projects, ensuring seamless data flow and alignment with business goals. Collaboration across various teams will be key to your success, as will your ability to translate technical challenges into business solutions.


Key Responsibilities


- Develop a modern, scalable data architecture using tools like Snowflake, Databricks, and Spark.

- Drive innovation and improve scalability by integrating cutting-edge technologies into the data infrastructure.

- Mentor junior engineers, guiding them to master the modern data stack and uphold best practices.

- Collaborate with product, operations, and business strategy teams to streamline data delivery.

- Ensure data privacy, security, and compliance with regulatory standards.


What We’re Looking For


- Strong understanding of data architecture, ETL/ELT processes, and data warehousing technologies (e.g., SQL Server, Snowflake, Databricks).

- Experience with cloud platforms (AWS, Azure, or Google Cloud) and big data technologies (Spark, Hadoop, Kafka).


Technical Skills


- Expertise in SQL, Python (Pandas, NumPy), and data modelling.

- Experience with data pipeline orchestration tools (e.g., Airflow, DBT).

- Proficiency in version control using git and CI/CD pipelines.

- Ability to pick up SSIS, SSRS, and SSAS technologies for legacy tech migration.

- Familiarity with data governance and data catalogue tools (e.g., Secoda, Alation) is desirable.


If you are ready to take the next step in your career as a Data Engineer and find this role compelling, please apply to this vacancy. Abigail Fernandes will reach out to discuss further.

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 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.

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.

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.