Analytics Engineer

TechNET IT
London
1 year ago
Applications closed

Related Jobs

View all jobs

Analytics Engineer: Data Architecture & Strategy Lead

Data Analytics Engineer

Data Engineer

Data Analytics Engineer

Data Analytics Engineer — Domo & SaaS Platform Lead

Data Analytics Engineer — Hybrid Data Pipelines

United Kingdom - London
Posted: 25/07/2024

Salary: £0.00to £500.00 per Day
ID: 34443_BH

Apply

ORAnalytics Engineer | 6-month contract initially | Hybrid role based in London
Our client is on a quest to modernize their data stack and expand their analytics engineering function. They are on the hunt for experienced Analytics Engineers enthusiastic about designing innovative solutions that balance bespoke in-house solutions with external tools, driving data governance, quality, and availability.
Responsibilities:You will bedeveloping robust data models within your domain, observing best practices and frameworks to improve data discoverability, accessibility, manipulation, and reporting. Serving as a data steward and owner of a decentralized data network, ensuring top-tier governance and maintaining comprehensive documentation across your domain's lineage. Partnering closely with the data engineering and ETL teams to translate technical requirements into non-technical outcomes and vice versa. Ensure consistent reporting by aligning stakeholders on definitions of metrics and dimensions within your domain. Drive the success of their self-service program by educating non-technical stakeholders on the usage of our tools. Design creative solutions and suggest new tools and methods to reduce friction in the data flow process, reflecting the needs of your domain.Skills & Experience:Looking for someone who is proficient at explaining complicated processes, requirements, and data pipelines to both technical and non-technical stakeholders, and then presenting this to various levels of stakeholder's seniority. Demonstrated experience in designing processes, frameworks, and best practices for junior teams to drive standardization and uniformity in data manipulation, documentation, and orchestration. Vast experience with multiple data visualization and transformation tools, including preference for DBT, Airflow, Fivetran, and Tableau/PowerBI/Looker. Certified or expert in dbt and its standards. Experience with QAing data pipelines using tools like Monte Carlo or similar. Proficient in documentation using Jira, Confluence, or similar platforms. Expert in SQL and Python, with the ability to upskill teams on best practices. Skilled in manipulating large datasets, combining data from multiple sources to achieve desired results. Working knowledge of design thinking and agile methodologies.

Apply

OR

Share:

Analytics Engineer

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 Jobs

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

Jobs

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Education Jobs

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

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

🍪 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.