Adobe Analyics Data Engineer

Cathedrals
9 months ago
Applications closed

Adobe Analyics Data Engineer 

Our client is unable to provide sponsorship. 

A leading Mar-Tech corporation is hiring a Adobe Analyics Data Engineer to join a team of technical consultants with a background in data science/analytics who has experience with Data warehousing concepts who has EXCELLENT communication capabilities. This is a junior to mid level position, where you will be given time to develop and enhance your capabilities in Power BI, Adobe Analytics, and Google Analytics/Python. Our client is paying a basic salary of £35,000 (circa) + a Quarterly Bonus of 5 to 10% + additional benefits to be based in London on a hybrid basis.

Key Responsibilities:

Analyze and optimize digital performance using tools like Adobe Analytics and Google Analytics
Implement and manage tracking solutions with Adobe Launch and data layers
Develop actionable insights from complex data sets and communicate them clearly to both technical and non-technical stakeholders
Build compelling dashboards and visualizations using Tableau and Power BI
Manage data workflows with ETL tools, and enhance data-driven decision-making
Work closely with clients to understand their business objectives and deliver tailored insights
Contribute to A/B testing, attribution modeling, and customer journey analysis effortsKey Skills & Experience:

Bachelor’s degree in Data Science, Analytics, Business, Marketing, or related field
Proven experience in a digital data role, ideally within a consultancy or client-facing environment is a must have
Expertise in Adobe Analytics, Adobe Launch, and Google Analytics is a must have
Familiarity with cloud-based data platforms (AWS, Google Cloud, Snowflake) is a must have
Hands-on experience with JavaScript and data layer implementations
Strong proficiency in Tableau and Power BI for data visualization
Knowledge of Tealium or other tag management systems
Solid understanding of ETL processes and data processing workflows
Strong client-facing communication skills and the ability to manage stakeholder expectations
Experience with A/B testing, attribution modeling, and customer journey analysis is a plusIf you're a problem-solver with a passion for data and analytics, we want to hear from you! Apply now and take the next step in your career

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 to Write a Data Engineering Job Ad That Attracts the Right People

Data engineering is the backbone of modern data-driven organisations. From analytics and machine learning to business intelligence and real-time platforms, data engineers build the pipelines, platforms and infrastructure that make data usable at scale. Yet many employers struggle to attract the right data engineering candidates. Job adverts often generate high application volumes, but few applicants have the practical skills needed to build and maintain production-grade data systems. At the same time, experienced data engineers skip over adverts that feel vague, unrealistic or misaligned with real-world data engineering work. In most cases, the issue is not a shortage of talent — it is the quality and clarity of the job advert. Data engineers are pragmatic, technically rigorous and highly selective. A poorly written job ad signals immature data practices and unclear expectations. A well-written one signals strong engineering culture and serious intent. This guide explains how to write a data engineering job ad that attracts the right people, improves applicant quality and positions your organisation as a credible data employer.

Maths for Data Engineering Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data engineering jobs in the UK, maths can feel like a vague requirement hiding behind phrases like “strong analytical skills”, “performance mindset” or “ability to reason about systems”. Most of the time, hiring managers are not looking for advanced theory. They want confidence with the handful of maths topics that show up in real pipelines: Rates, units & estimation (throughput, cost, latency, storage growth) Statistics for data quality & observability (distributions, percentiles, outliers, variance) Probability for streaming, sampling & approximate results (sketches like HyperLogLog++ & the logic behind false positives) Discrete maths for DAGs, partitioning & systems thinking (graphs, complexity, hashing) Optimisation intuition for SQL plans & Spark performance (joins, shuffles, partition strategy, “what is the bottleneck”) This article is written for UK job seekers targeting roles like Data Engineer, Analytics Engineer, Platform Data Engineer, Data Warehouse Engineer, Streaming Data Engineer or DataOps Engineer.

Neurodiversity in Data Engineering Careers: Turning Different Thinking into a Superpower

Every modern organisation runs on data – but without good data engineering, even the best dashboards & machine learning models are built on sand. Data engineers design the pipelines, platforms & tools that make data accurate, accessible & reliable. Those pipelines need people who can think in systems, spot patterns in messy logs, notice what others overlook & design elegant solutions to complex problems. That is exactly why data engineering can be such a strong fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & considering a data engineering career, you might have heard comments like “you’re too disorganised for engineering”, “too literal for stakeholder work” or “too distracted for complex systems”. In reality, the traits that can make traditional office environments hard often line up beautifully with data engineering work. This guide is written for data engineering job seekers in the UK. We’ll cover: What neurodiversity means in a data engineering context How ADHD, autism & dyslexia strengths map to common data engineering tasks Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data engineering – & how to turn “different thinking” into a genuine professional superpower.