Senior Data Engineer, SQL, RDBMS, AWS, Python, Mainly Remote

Holborn and Covent Garden
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer (Big Data/ Hadoop/ Spark) (Banking)

Senior Data Engineer - (Python & SQL)

Senior Data Engineer - (Python & SQL)

Data Engineer - (Python, SQL, Machine Learning) - Robotics

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer, SQL, RDBMS, Python, Celery, RabbitMQ, AWS, Part Central London, Mainly Remote

Senior Data Engineer (SQL, RDBMS, Python, AWS) required to work for a fast growing and exciting business based in Central London. However, this role is mainly remote.

We need an experienced Data Developer who is a good people person, working with client facing teams outside of Technology, and also mentoring more junior members of the team across Europe. As the company is fast growing, there will be an opportunity to move upwards at certain points throughout your journey. Read on for more details…

Responsibilities

  • Collaborate with product managers and business stakeholders to understand complex business requirements to translate business needs into well-designed and maintainable solutions

  • Ensure data quality and reliability by implementing robust data quality checks, monitoring, and alerting to ensure the accuracy and timeliness of all data pipelines

  • Create data governance policies and develop data models and schemas optimized for analytical workloads

  • Influence the direction for key infrastructure and framework choices for data pipelining and data management

  • Manage complex initiatives by setting project priorities, deadlines, and deliverables

  • Collaborate effectively with distributed team members across multiple time zones, including offshore development teams

    Skills required:

  • Proven track record building scalable data pipelines (batch and streaming) in production

  • Expert Python, PySpark, Celery and RabbitMQ skills; deep experience with AWS data stack (Glue, OpenSearch, RDS)

  • Expert skills within SQL with experience in both transactional RDBMS systems and distributed systems

  • Hands-on with Lakehouse technologies (Apache Iceberg, S3 Tables, StarRocks)

  • Strong grasp of data governance, schema design, and quality frameworks

  • Comfortable leading infrastructure decisions and collaborating across distributed teams

    This is a fantastic opportunity and salary is dependent upon experience. Apply now for more details

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.

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.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 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.