AWS Data Engineer - Amazon Web Services

Farringdon
2 months ago
Applications closed

Related Jobs

View all jobs

AWS Data Engineer (Glue, Redshift, Athena, OpenSight)

Senior Data Engineer

Senior Data Engineer

Data Engineer

Geospatial Data Engineer

Lead Data Engineer

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

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.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.