Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

AWS Data Engineer - Amazon Web Services

Farringdon
9 months ago
Applications closed

Related Jobs

View all jobs

AWS Data Engineer

Data Engineer – SC Cleared - AWS

Senior Data Engineer

Data Analyst (Banking Domain)

Senior Data Engineer – AWS Cloud & Data Platforms

Data Engineer – AWS | Hybrid | Meaningful Projects Across Multiple Sectors

My client is a Global IT Consultancy, who are currently looking for multiple Data Engineers to join their team. This is a permanent position and represents a unique opportunity for someone to enhance their digital career.

AWS Data Engineer

Salary guideline: £60,000 - £85,000 pa + pension up to 6% contributory, Health Insurance, Life Assurance etc.

Base Location: UK Wide - Hybrid model

The Client:

We are excited to be offering this opportunity for a talented AWS DATA Engineer to join my clients rapidly expanding team. My client is a Global IT Consultancy, who are currently looking for multiple Data Engineers to join their teams in London and Manchester. This is a permanent position and represents a unique opportunity for someone to enhance their digital career.

The Role:

Essential Skills and Experience:

Have a deep, hands-on design and engineering background in AWS, across a wide range of AWS services with the ability to demonstrate working on large engagements

Experience of AWS tools (e.g Athena, Redshift, Glue, EMR)
Java, Scala, Python, Spark, SQL
Experience of developing enterprise grade ETL/ELT data pipelines.
Deep understanding of data manipulation/wrangling techniques
Demonstrable knowledge of applying Data Engineering best practices (coding practices to DS, unit testing, version control, code review).
Big Data Eco-Systems, Cloudera/Hortonworks, AWS EMR, GCP DataProc or GCP Cloud Data Fusion.
NoSQL Databases. Dynamo DB/Neo4j/Elastic, Google Cloud Datastore.
Snowflake Data Warehouse/Platform
Streaming technologies and processing engines, Kinesis, Kafka, Pub/Sub and Spark Streaming.
Experience of working with CI/CD technologies, Git, Jenkins, Spinnaker, GCP Cloud Build, Ansible etc
Experience building and deploying solutions to Cloud (AWS, Google Cloud) including Cloud provisioning tools
Have hands on experience with Infrastructure-as-Code technologies: Terraform, Ansible
Capable of working in either an agile or Waterfall development environment, both as part of a team and individually
E2E Solution Design skills - Prototyping, Usability testing
Experience with SQL and NoSQL modern data stores
Strong interpersonal skills with the ability to work with clients to establish requirements in non-technical language.
Ability to translate business requirements into plausible technical solutions for articulation to other development staff.
Good understanding of Data Governance, including Master Data Management (MDM) and Data Quality tools and processes
Influencing and supporting project delivery through involvement in project/sprint planning and QAAlso:

Knowledge of other cloud platforms
Google Data Products tools knowledge (e.g. BigQuery, Dataflow, Dataproc, AI Building Blocks, Looker, Cloud Data Fusion, Dataprep, etc.) Relevant certifications
Python
Snowflake
Databricks To apply please click the "Apply" button and follow the instructions.

For a further discussion, please contact Aaron Perdesi on (phone number removed).

83zero Consulting Limited is a boutique consultancy specialising in Software Development & Agile within the UK. We provide high quality interim and permanent senior IT professionals

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.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.