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

Senior Data Engineer - (Python & SQL)

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

Senior Data Engineer

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.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.