Data, Analytics & AI

A Data Analyst collects, cleans, and interprets structured and unstructured data to help organisations make better decisions. Core work includes writing SQL queries, building dashboards and reports in tools like Power BI or Tableau, conducting statistical analysis, and communicating findings clearly to non-technical stakeholders. Analysts work across departments — marketing, operations, finance, product — translating business questions into data queries and turning results into actionable insight. The role sits on a spectrum between reporting (descriptive analytics) and more advanced analysis involving predictive modelling, A/B testing, or machine learning. Modern Data Analysts are expected to be proficient in SQL and at least one scripting language (typically Python or R), comfortable with cloud data platforms (e.g. BigQuery, Snowflake, Databricks), and capable of designing analyses that are reproducible and well-documented. Data storytelling — presenting findings in a way that influences decisions — is an equally important skill.

A Business Analyst (BA) acts as a bridge between business stakeholders and technical delivery teams. The role involves eliciting and documenting requirements, mapping current and future-state processes, identifying inefficiencies, and translating business needs into specifications that developers, architects, or vendors can act on. BAs facilitate workshops, write user stories, produce process flow diagrams, and manage stakeholder sign-off through the project lifecycle. The role varies significantly by organisation and methodology. In agile environments, BAs often work closely with product owners, writing and grooming backlogs. In more traditional project settings, they produce formal requirements documents and business cases. Increasingly, BAs are expected to work with data — understanding system integrations, validating data mappings, and using tools like SQL or Power BI to interrogate outputs and support decisions.

A BI (Business Intelligence) Developer designs, builds, and maintains the reporting and analytics infrastructure that organisations use to turn raw data into actionable insight. Day-to-day work involves developing dashboards and visualisations in tools like Power BI, Tableau, or Looker, writing SQL queries to extract and transform data from databases and data warehouses, working with business stakeholders to understand their information needs, and maintaining the data models and pipelines that underpin reporting. BI Developers sit at the intersection of data engineering and data analysis — they need to understand both the technical data infrastructure and the business questions it is meant to answer. Entry-level positions often carry titles such as Junior BI Developer, BI Analyst, or Reporting Analyst, and focus on maintaining existing dashboards, writing SQL for ad hoc analysis, and gradually taking on more complex development work under senior oversight. In the UK, BI Developer roles exist across almost every sector — financial services, retail, healthcare, public sector, and technology — making this one of the most transferable analytical skill sets available. Employers increasingly expect proficiency in cloud data platforms (Azure Synapse, AWS Redshift, Google BigQuery) alongside the core BI tooling, reflecting the rapid migration of data infrastructure to the cloud.

A Data Quality Analyst ensures that the data an organisation relies on for decisions, reporting, and operations is accurate, complete, consistent, and fit for purpose. Day-to-day work involves profiling datasets to identify anomalies and errors, designing and running data quality checks and validation rules, investigating the root causes of data issues, working with data engineers and business teams to remediate problems, and maintaining data quality metrics dashboards. The role sits at a critical juncture between data engineering, data governance, and business operations — data quality issues that go undetected cost organisations in bad decisions, regulatory penalties, and lost customer trust. Entry-level positions typically focus on running existing quality checks, investigating flagged issues, and documenting findings. As analysts develop, they take on more of the rule design, root cause analysis, and stakeholder engagement involved in building a proactive data quality programme. In the UK, demand has grown significantly as GDPR requirements, regulatory reporting obligations, and the expansion of data-driven decision-making have made poor data quality a material business risk. Analysts with SQL proficiency and an understanding of data pipelines are the most sought-after at entry level.

An AI Prompt Engineer designs, tests, and refines the instructions — known as prompts — that guide large language models (LLMs) to produce accurate, relevant, and safe outputs for a specific use case. Day-to-day work involves writing and iterating on prompts for production AI applications, running structured evaluations to measure how well prompts perform against defined quality criteria, collaborating with software engineers and product managers to integrate AI outputs into products and workflows, and documenting prompt strategies, failure modes, and evaluation results. The role requires both technical curiosity — understanding how LLMs behave and why — and strong written communication skills, since the primary tool is language itself. AI Prompt Engineering is one of the newest roles in technology, emerging alongside the mainstream adoption of LLMs from 2022 onwards. In the UK, it exists primarily at technology companies, AI-native startups, large enterprises running AI transformation programmes, and consulting firms advising clients on AI adoption. The role is still evolving — some organisations treat it as a standalone specialism, others embed it within product, data science, or content teams. Entry-level positions focus on prompt iteration and evaluation, while senior practitioners work on systematic evaluation frameworks, retrieval-augmented generation (RAG) architecture, and model selection decisions.

An MLOps Support Analyst helps an organisation deploy, monitor, and maintain machine learning models in production, bridging the gap between the data science team that builds models and the engineering and operations teams that keep systems running reliably. Day-to-day work involves monitoring model performance metrics to detect when predictions are degrading, supporting incident investigations when model behaviour changes unexpectedly, maintaining the pipelines that retrain and redeploy models, documenting model behaviour and known failure modes, and working with data scientists and engineers to improve the reliability of ML infrastructure. The role requires a combination of analytical rigour — understanding what makes a model's output trustworthy — and operational discipline around software systems and pipelines. MLOps (Machine Learning Operations) as a discipline emerged from the recognition that deploying a model is only the beginning: production ML systems require ongoing monitoring, retraining, and maintenance to remain accurate as the world changes around them. Entry-level MLOps Support Analyst positions are relatively rare — the function is often absorbed by data scientists or platform engineers in smaller organisations — but as ML deployments scale, dedicated operational support becomes essential. The role exists primarily at technology companies, financial services firms with large ML estates, and organisations running AI transformation programmes. It is a strong entry point into a broader ML engineering or data science career.

An Automation / RPA Analyst identifies, designs, and supports the deployment of software robots and automated workflows that replace or augment repetitive manual processes in an organisation. Day-to-day work involves working with business teams to map current processes, assess their suitability for automation, document requirements for robotic process automation (RPA) tools such as UiPath, Automation Anywhere, or Blue Prism, supporting the configuration and testing of bots, and monitoring deployed automations to ensure they continue to function correctly as underlying systems change. The role sits at the intersection of business analysis and technical implementation — Automation Analysts must understand processes well enough to redesign them, and understand the tooling well enough to specify and validate solutions. Entry-level positions typically focus on process discovery, documentation, and testing support, progressing toward bot development and ownership of the automation pipeline. The function exists across financial services, healthcare, the public sector, utilities, and retail — anywhere manual, rule-based processes are consuming significant operational capacity. Organisations running large automation programmes often employ dedicated RPA analysts alongside a Centre of Excellence that sets standards and governs the pipeline. Analysts who develop both process redesign skills and hands-on RPA tool proficiency progress fastest, as the ability to bridge business and technical perspectives is the core value of the role.

A Sustainability Analyst researches, measures, and reports on an organisation's environmental, social, and governance (ESG) performance, helping leadership understand their sustainability risks and opportunities and supporting compliance with an expanding body of disclosure requirements. Day-to-day work involves collecting and validating environmental data — energy consumption, greenhouse gas emissions, water use, waste — calculating carbon footprints across Scope 1, 2, and 3 categories, preparing sustainability reports aligned to frameworks such as GRI, TCFD, or the new IFRS Sustainability Disclosure Standards, supporting net zero target-setting, and engaging with internal teams to embed sustainability into business decisions. The role sits at the junction of data analysis, stakeholder communication, and regulatory compliance. Entry-level positions typically focus on data collection, carbon accounting, and report production, progressing toward strategy development, materiality assessments, and external engagement with investors and regulators. Sustainability Analyst roles exist across virtually every sector — financial services, energy, manufacturing, retail, real estate, and the public sector all have significant sustainability reporting obligations — and the function has grown rapidly as regulatory pressure, investor expectations, and corporate net zero commitments have elevated sustainability from a communications exercise to a core governance function. Analysts who combine quantitative data skills with a solid understanding of ESG frameworks and the regulatory landscape are consistently in demand.

An Energy Data Analyst collects, processes, and analyses data on energy consumption, generation, and costs to help organisations reduce their energy spend, cut emissions, and meet regulatory reporting obligations. Day-to-day work involves gathering half-hourly electricity and gas meter data, building dashboards and reports that track consumption patterns and identify anomalies, calculating carbon emissions from energy use, supporting energy procurement and contract management, contributing to ISO 50001 energy management systems, and working with operations teams to identify and quantify energy efficiency opportunities. The role requires a combination of technical data skills and enough understanding of energy systems — metering, billing, tariff structures, grid dynamics — to interpret consumption data accurately. Energy Data Analyst roles exist across a wide range of organisations: energy suppliers, grid operators, large energy users in manufacturing and retail, energy consultancies, public sector bodies including NHS trusts and local authorities, and the growing market of energy management service providers. The UK's legally binding net zero target and the dramatic expansion of renewable energy generation have made energy data more complex and more valuable simultaneously — the rise of flexibility markets, smart metering, and behind-the-meter generation means there is far more data to analyse and far more value in doing so rigorously. Analysts who can work with large time-series datasets and connect technical energy analysis to commercial and regulatory outcomes are consistently in demand.