Data & Analytics

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 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.

A Junior Actuary applies statistical and mathematical techniques to assess financial risk and uncertainty, typically within insurance, pensions, investment management, or financial services. Day-to-day work involves building and running actuarial models, analysing large datasets, preparing reports for senior actuaries and management, supporting reserving calculations, and contributing to pricing or capital modelling projects. Junior Actuaries work under the supervision of qualified actuaries and are expected to be progressing through the Institute and Faculty of Actuaries (IFoA) examinations throughout their early career. The role demands strong mathematical and statistical ability combined with commercial awareness and clear communication skills — actuaries must be able to explain complex probabilistic analysis to non-technical stakeholders including boards and regulators. In the UK, actuarial roles are most concentrated in the London market (Lloyd's of London, general and life insurance), Edinburgh (life and pensions), and the major consulting firms. Demand is growing in newer areas including cyber insurance, climate risk, and data science applications, meaning the discipline is evolving well beyond its traditional insurance heartland.

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.

A Healthcare Data Analyst extracts, processes, and analyses clinical and operational data to help healthcare organisations understand patient outcomes, improve service delivery, and meet reporting obligations. Day-to-day work involves querying clinical databases and patient administration systems, producing performance reports for NHS commissioners, trust boards, and clinical teams, supporting audit and quality improvement projects, and working with clinicians and managers to translate data findings into actionable insights. The role requires a combination of technical data skills and enough healthcare domain knowledge to interpret clinical data accurately and communicate findings to clinical audiences. Healthcare Data Analyst roles exist across NHS trusts, GP practices, clinical commissioning groups (now Integrated Care Boards), NHS England, Public Health England successor bodies, and private healthcare providers. The function has grown significantly as the NHS has invested in its analytical capability following the NHS Long Term Plan's commitment to becoming a data-driven health service. Data analysts working in healthcare must navigate a particularly complex data governance environment — patient data is sensitive, heavily regulated under GDPR and the Data Security and Protection Toolkit, and subject to strict information governance controls. Analysts who combine SQL proficiency with healthcare data literacy — understanding of SNOMED codes, ICD-10 coding, HES data, and NHS data standards — are consistently in demand.

A Health Informatics Specialist works at the intersection of healthcare, information technology, and data management — designing, implementing, and improving the clinical information systems that NHS and healthcare organisations rely on to manage patient care, share clinical data, and meet reporting obligations. Day-to-day work involves supporting the configuration and optimisation of electronic patient record (EPR) systems, managing clinical coding systems and terminology standards, supporting data quality improvement programmes, working with clinical teams to understand their information needs, and ensuring that clinical data flows accurately between systems. The role requires a rare combination of clinical domain knowledge, data literacy, and IT systems understanding. Health informatics roles exist across NHS trusts, primary care networks, Integrated Care Boards, NHS England, and in the private sector at health IT vendors (Epic, EMIS, SystemOne) and consultancies. The profession has grown significantly with the NHS's digital transformation programme — the ambition to become a paperless health service has required widespread EPR implementation, digital diagnostics, and interoperability between previously siloed clinical systems. Specialists who can bridge the clinical, data, and technology perspectives are among the most valuable professionals in the health technology sector.

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.