Databricks SME and AWS Data Engineer

Northampton

2 months ago

Applications closed

Related Jobs

View all jobs

Urgent | Contract Senior Data Engineer - Azure + Databricks + Snowflake

Data Scientist – Marketing Effectiveness – £65k–£95k – MMM – AI

Data Architect

Data Engineer

Data Architect - Bristol - Hybrid Opportunity

Data Analyst

Databricks SME and AWS Data Engineer
Location: UK - Northampton (Hybrid)
6 months

UMBRELLA ONLY

Project Objective:
A key initiative involves migrating from Aerospike to Postgres and leveraging Databricks for back-testing fraud detection models. This role will contribute to the development and integration of Proof of Concepts (PoCs) from the detection backlog.

Key Responsibilities:

Collaborate with cross-functional teams to architect, design, develop and deliver PoCs related to fraud detection.
Lead the ETL & Data manipulation/engineering work on AWS.
Integrate Databricks-based back-testing into the fraud detection pipeline.
Work closely with architects and other developers to ensure seamless integration with existing systems.
Participate in weekly stand-up calls to demonstrate progress and align on deliverables.
Take ownership of tasks from the backlog, ensuring timely and high-quality delivery.
Required Skills & Experience:

Strong AWS Data Engineering expertise.
Proficiency in Kafka for real-time data streaming and integration.
Proficiency with Databricks for data processing and analytics.
Working knowledge of NodeJS would be an added advantage.
Solid programming skills in Python, PySpark, Spark.
Candidate must be adept at working with large-scale datasets, S3, Python, and data cataloguing tools. Familiarity with data engineering best practices is essential.
Performance and Code Quality - candidate should demonstrate a strong commitment to building high-performance, scalable, and resilient distributed systems, with an emphasis on clean, maintainable, and testable code.
CI/CD Proficiency - Hands-on experience with CI/CD pipelines and tools (e.g., Jenkins, GitHub Actions, GitLab CI, etc.) for automated build, test, and deployment processes.
Secure Development Practices - Awareness of secure coding standards, data protection principles, and experience working in regulated environments (especially relevant for fraud detection and financial services).
Testing Rigor - Strong understanding of unit testing, integration testing, and test automation frameworks (e.g., PyTest, etc.) to ensure code quality and reliability.
Familiarity with cloud-native development and CI/CD practices.
Agile mindset with a proactive, developer-driven approach to problem-solving.
Ideal Candidate Profile:

A hands-on architect & developer with a strong sense of ownership and accountability.
Comfortable working in a collaborative, fast-paced environment.
Able to pick up tasks independently, contribute to design discussions, and deliver integrated solutions.
Strong communication skills to engage with both technical and non-technical stakeholders.All profiles will be reviewed against the required skills and experience. Due to the high number of applications we will only be able to respond to successful applicants in the first instance. We thank you for your interest and the time taken to apply

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Apr 9, 2026

Products

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

Mar 24, 2026

Jobs

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

Feb 10, 2026

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.