SC Data Engineer - AWS

LA International
London
1 year ago
Applications closed

Related Jobs

View all jobs

SC Cleared Data Engineer

SC cleared Data Engineer - Talend and Oracle RDS

Data Engineer (SC Cleared)

Data Engineer

SC Cleared Data Architect

SC Cleared Data Architect

SC Data Engineer - AWS

IR35: Outside
Rate: £400-450 / day (negotiable)
Clearance: SC
Start: ASAP
Duration: 6 months (extensions expected)
Location: Remote with occasional travel to Bristol / London

Job Brief: We are seeking a skilled and motivated Data Engineer with expertise in AWS to join our dynamic team. The ideal candidate will be responsible for designing, building, and maintaining scalable data pipelines that process vast amounts of data across different platforms. They will leverage AWS technologies to ensure seamless integration and optimization of data flows, ensuring high availability, security, and efficiency.

Skills & Qualifications:
* Strong experience with AWS services such as S3, Redshift, EC2, Lambda, Glue, Athena, and EMR.
* Proficiency in programming languages such as Python, Java, or Scala for data engineering tasks.
* Experience with data warehousing, ETL (Extract, Transform, Load) processes, and data integration.
* In-depth knowledge of cloud-based architectures and best practices for deploying data pipelines in AWS.
* Expertise in designing and implementing scalable and efficient data pipelines for both batch and real-time processing.
* Hands-on experience with data transformation and data processing frameworks (e.g., Apache Spark, Apache Kafka).
* Solid understanding of relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., DynamoDB, MongoDB).
* Familiarity with data security, governance, and compliance standards, particularly in cloud environments.
* Strong problem-solving skills and attention to detail.
* Excellent communication skills for collaboration with cross-functional teams.
* Ability to work in an agile development environment and manage multiple priorities.

Preferred Qualifications:
* Experience with containerization technologies like Docker and Kubernetes.
* Knowledge of data lakes, serverless architectures, and microservices.
* Familiarity with DevOps practices and CI/CD pipelines for automated deployment of data solutions.
* Certifications such as AWS Certified Data Analytics - Specialty or AWS Certified Solutions Architect are a plus.

Responsibilities:
* Design, develop, and optimize data pipelines on AWS to ingest, process, and transform data.
* Collaborate with data scientists, analysts, and business teams to understand requirements and deliver data solutions.
* Implement and manage efficient data storage solutions using AWS technologies.
* Ensure data quality, security, and compliance across all data engineering processes.
* Continuously monitor and improve data systems to ensure scalability and performance.
* Contribute to the development and implementation of best practices and standards for data engineering.

APPLY NOW!


Due to the nature and urgency of this post, candidates holding or who have held high level security clearance in the past are most welcome to apply. Please note successful applicants will be required to be security cleared prior to appointment which can take up to a minimum 10 weeks.

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.