Data Analyst

Data Analyst

Interpret data to guide decision-making and solve business problems.

Data & Analytics
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Average salary in Australia
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Data Analysts draw meaningful insights from complex datasets with the goal of making better decisions. Data Analysts work wherever an organisation has data - these days that could be in any function, such as product, sales, marketing, HR, operations and more. Data Analysts will normally be well skilled in querying relational databases using SQL, wrangling & cleaning datasets, conducting analysis and creating simple to read reports & visualisations for the end decision makers. They’ll commonly use a programming language like Python or R to conduct their analysis, and also BI tools like Excel, Tableau, PowerBI and Looker.

Data Analysts are increasingly common positions in modern companies. The digitization of so many industries has seen an explosion in the supply of data generated, creating an enormous opportunity to make better, data-informed decisions. With it, companies then have had to hire people to make sense of the data.

Data Analysts are one of the core roles in any data team. The scope of the role depends quite a bit on the size & industry of the company, and their relative data maturity.

Core Data Analyst Required Skills

.NET.NETA/B TestingA/B TestingAccessibilityAccessibilityAdaptabilityAdaptabilityAdobe AnalyticsAdobe AnalyticsAdobe PhotoshopAdobe PhotoshopAdobe TargetAdobe TargetAdvanced AnalyticsAdvanced AnalyticsAgileAgileAirtableAirtableAlgorithmsAlgorithmsAlteryx DesignerAlteryx DesignerAmazon AthenaAmazon AthenaAmazon AuroraAmazon AuroraAmazon DynamoDBAmazon DynamoDBAmazon KinesisAmazon KinesisAmazon Web ServicesAmazon Web ServicesAmplitude AnalyticsAmplitude AnalyticsAnalytical MindsetAnalytical MindsetAnalytical ReasoningAnalytical ReasoningAnalytics DatabasesAnalytics DatabasesAnalytics ProgrammingAnalytics ProgrammingAnalytics Project ManagementAnalytics Project ManagementAnomaly DetectionAnomaly DetectionApache BeamApache BeamApache HiveApache HiveArea ChartsArea ChartsAssociation RulesAssociation RulesAttention to DetailAttention to DetailAutomated Data Quality ChecksAutomated Data Quality ChecksAvailability HeuristicAvailability HeuristicAzure Data LakeAzure Data LakeBar ChartsBar ChartsBayes TheoremBayes TheoremBayesian AnalysisBayesian AnalysisBehavioral AnalyticsBehavioral AnalyticsBERTBERTBiasBiasBig DataBig DataBig Data MiningBig Data MiningBinary SearchBinary SearchBinomial DistributionBinomial DistributionBonferroni CorrectionBonferroni CorrectionBoxplotsBoxplotsBusiness AcumenBusiness AcumenBusiness AnalyticsBusiness AnalyticsBusiness InsightsBusiness InsightsBusiness IntelligenceBusiness IntelligenceBusiness Intelligence DevelopmentBusiness Intelligence DevelopmentBusiness StrategyBusiness StrategyCCC++C++Causal InferenceCausal InferenceCause & EffectCause & EffectCentral Limit TheoremCentral Limit TheoremChart InterpretationChart InterpretationChi-Squared DistributionChi-Squared DistributionClassificationClassificationClassification MetricsClassification MetricsCloud AnalyticsCloud AnalyticsClusteringClusteringCode ReviewsCode ReviewsCollaborationCollaborationCollinearityCollinearityColumn ChartsColumn ChartsColumnar DatabasesColumnar DatabasesCommunicationCommunicationComparatorsComparatorsCompassionCompassionConditional ProbabilityConditional ProbabilityConfidence IntervalsConfidence IntervalsConfirmation BiasConfirmation BiasConflict ManagementConflict ManagementConfusion MatricesConfusion MatricesContent Management SystemsContent Management SystemsContinuous VariablesContinuous VariablesControl StructuresControl StructuresConvolutionConvolutionCorrelationCorrelationcsv filescsv filesCuriosityCuriosityCustomer Data PlatformsCustomer Data PlatformsD3.jsD3.jsDashboardingDashboardingDaskDaskDataDataData AcquisitionData AcquisitionData AdvocacyData AdvocacyData AnalysisData AnalysisData AnonymizationData AnonymizationData BlendingData BlendingData EntryData EntryData EthicsData EthicsData ExplorationData ExplorationData FormatsData FormatsData GovernanceData GovernanceData IntegrationData IntegrationData InterpretationData InterpretationData LakeData LakeData LakehouseData LakehouseData LeakageData LeakageData LineageData LineageData LiteracyData LiteracyData ManagementData ManagementData ManipulationData ManipulationData MiningData MiningData MonitoringData MonitoringData PrivacyData PrivacyData ProcessingData ProcessingData ScrapingData ScrapingData SecurityData SecurityData StewardshipData StewardshipData StorytellingData StorytellingData StrategyData StrategyData StreamingData StreamingData TransformationsData TransformationsData TypesData TypesData VisualizationData VisualizationData WarehousingData WarehousingData WranglingData WranglingData-Driven Decision MakingData-Driven Decision MakingData-Driven InsightsData-Driven InsightsDatabase ManagementDatabase ManagementDatabase Management ToolDatabase Management ToolDatabricksDatabricksDataFramesDataFramesDAXDAXDecision TreesDecision TreesDendrogramsDendrogramsDenial of ServiceDenial of ServiceDependency GraphsDependency GraphsDifference in DifferencesDifference in DifferencesDigital AnalyticsDigital AnalyticsDimension TablesDimension TablesDimensional ModellingDimensional ModellingDistance MatricesDistance MatricesDistance MetricsDistance MetricsDistributed ComputingDistributed ComputingDistributed Data ProcessingDistributed Data ProcessingDistributed SQL Query EngineDistributed SQL Query EngineDistributionsDistributionsDomoDomodplyrdplyrEconometric ModelingEconometric ModelingElasticityElasticityElasticsearchElasticsearchEncryptionEncryptionEnglishEnglishEntropyEntropyErlangErlangError of DecompositionError of DecompositionEvaluation MetricsEvaluation MetricsEvent AnalyticsEvent AnalyticsEvent Data AnalysisEvent Data AnalysisExploratory Data AnalysisExploratory Data AnalysisExtroversionExtroversionFact TablesFact TablesFactual AccuracyFactual AccuracyFeature DependenciesFeature DependenciesFew-Shot PromptingFew-Shot PromptingFinancial ModelingFinancial ModelingFor LoopsFor LoopsForecastingForecastingFormulasFormulasFrequency GraphsFrequency GraphsGgplot2Ggplot2Google AnalyticsGoogle AnalyticsGoogle BigQueryGoogle BigQueryGoogle SheetsGoogle SheetsGoogle Tag ManagerGoogle Tag ManagerGradientsGradientsGrafanaGrafanaGraphsGraphsGrowth MindsetGrowth MindsetHeat MapsHeat MapsHeteroscedasticityHeteroscedasticityHistogramsHistogramsHMMHMMHomoscedasticityHomoscedasticityHotjarHotjarHypothesis TestingHypothesis TestingIBM Db2IBM Db2Illusory CorrelationIllusory CorrelationImputationImputationIndexingIndexingIndustriousnessIndustriousnessInformaticaInformaticaInformation RetrievalInformation RetrievalInteractive Query ServiceInteractive Query ServiceIteratorsIteratorsJuliaJuliaJupyter NotebookJupyter NotebookK-MeansK-MeansKanbanKanbanKNIMEKNIMEKnowledge GraphsKnowledge GraphsKotlinKotlinLanguage ModelingLanguage ModelingLean MethodologyLean MethodologyLFSLFSLine ChartsLine ChartsLinear ExtrapolationLinear ExtrapolationLinear Model AnalysisLinear Model AnalysisLinear ModellingLinear ModellingLLMsLLMsLog CollectionLog CollectionLogistic RegressionsLogistic RegressionsLookerLookerLooker StudioLooker StudioLoopsLoopsLSILSILuaLuaMacrosMacrosManaging UpManaging UpMarket Basket AnalysisMarket Basket AnalysisMarket ResearchMarket ResearchMarketing AnalyticsMarketing AnalyticsMarkov ChainsMarkov ChainsMathematicsMathematicsMATLABMATLABMatricesMatricesMeasures of Central TendencyMeasures of Central TendencyMeasures of DispersionMeasures of DispersionMetaBaseMetaBaseMetricsMetricsMicrosoft AccessMicrosoft AccessMicrosoft ExcelMicrosoft ExcelMinimum Remaining ValuesMinimum Remaining ValuesMissing Value TreatmentMissing Value TreatmentMitigating BiasesMitigating BiasesMixpanelMixpanelMode AnalyticsMode AnalyticsModel BiasModel BiasMouseflowMouseflowMoving AveragesMoving AveragesMulti-factor AuthenticationMulti-factor AuthenticationMultivariate StatisticsMultivariate StatisticsMVCMVCNaive BayesNaive BayesNatural Language ProcessingNatural Language ProcessingNested LoopsNested LoopsNeuroticismNeuroticismNo Code DatabaseNo Code DatabaseNormal DistributionNormal DistributionNoSQL DatabasesNoSQL DatabasesNumerical ReasoningNumerical ReasoningNumPyNumPyOAuth2OAuth2Objective-CObjective-COIDCOIDCOLAPOLAPOLTPOLTPOne-Hot EncodingOne-Hot EncodingOperation AnalyticsOperation AnalyticsOptimizationOptimizationOracle Business Intelligence Enterprise Edition PlusOracle Business Intelligence Enterprise Edition PlusOracle DatabaseOracle DatabaseOrganisational AnalyticsOrganisational AnalyticsOutlier RemovalOutlier RemovalOutlier TreatmentOutlier TreatmentOutliersOutliersP-ValueP-ValuePandasPandasPassword HandlingPassword HandlingPie ChartsPie ChartsPivot TablesPivot TablesPlotlyPlotlyPower BIPower BIPowerPointPowerPointPowerShellPowerShellPre-processingPre-processingPrescriptive AnalyticsPrescriptive AnalyticsPresentationsPresentationsPrincipal Component AnalysisPrincipal Component AnalysisProbabilityProbabilityProbability DensityProbability DensityProbability DistributionsProbability DistributionsProblem SolvingProblem SolvingProduct AnalyticsProduct AnalyticsProject ManagementProject ManagementPrompt EngineeringPrompt EngineeringPrototypingPrototypingQlikQlikQualitative ResearchQualitative ResearchQuality AssuranceQuality AssuranceQuantitative ResearchQuantitative ResearchQuery OptimisationQuery OptimisationQuickSightQuickSightRRR^2R^2Radar ChartsRadar ChartsRatiosRatiosRecency BiasRecency BiasRecommendation SystemsRecommendation SystemsRegression ModelsRegression ModelsRegressionsRegressionsRegular ExpressionsRegular ExpressionsRelational DatabasesRelational DatabasesReportingReportingRequirements GatheringRequirements GatheringRequirements TranslationRequirements TranslationReverting ChangesReverting ChangesRidge RegressionRidge RegressionRisk AnalysisRisk AnalysisROCROCSales AnalyticsSales AnalyticsSamplingSamplingSampling BiasSampling BiasSAP HANASAP HANASASSASScatter ChartsScatter ChartsSeabornSeabornSearch EnginesSearch EnginesSearching ArraysSearching ArraysSeasonality AnalysisSeasonality AnalysisSegmentationSegmentationServerless ComputingServerless ComputingSisenseSisenseSisense for Cloud Data TeamsSisense for Cloud Data TeamsSoftware EngineeringSoftware EngineeringSolarWindsSolarWindsSolution DesignSolution DesignSortingSortingSpatial ReasoningSpatial ReasoningSplunkSplunkSpreadsheetsSpreadsheetsSPSSSPSSSQLSQLSQL DevelopmentSQL DevelopmentSSASSSASStandard DeviationStandard DeviationStandardizationStandardizationStataStataStatistical MeasuresStatistical MeasuresStatistical ModellingStatistical ModellingStatisticsStatisticsStrategic InsightsStrategic InsightsStrategic ThinkingStrategic ThinkingStrategies for Missing DataStrategies for Missing DataStructured DataStructured DataSummary StatsSummary StatsSupermetricsSupermetricsSurvival AnalysisSurvival AnalysisSwiftSwiftT-TestsT-TestsTableauTableauTablesTablesTask ManagementTask ManagementTechnical WritingTechnical WritingText PreprocessingText PreprocessingThe Big Five Personality ModelThe Big Five Personality ModeltidyrtidyrtidyversetidyverseTime Series AnalysisTime Series AnalysisTopic ModelingTopic ModelingTreemapsTreemapsTrelloTrelloTrend AnalysisTrend AnalysisTrinoTrinoTuplesTuplesType 1 ErrorType 1 ErrorType 2 ErrorType 2 ErrorTypes of DataTypes of DataTypes of ErrorsTypes of ErrorsUnderfittingUnderfittingUnixUnixUnstructured DataUnstructured DataUnsupervised AlgorithmsUnsupervised AlgorithmsUnsupervised LearningUnsupervised LearningUsability TestingUsability TestingUser ExperienceUser ExperienceUserflowUserflowVarianceVarianceVBAVBAVerbal ReasoningVerbal ReasoningVerticaVerticaViewsViewsVirusesVirusesVLOOKUPVLOOKUPVolatilityVolatilityWaterfallWaterfallWaterfall ChartsWaterfall ChartsWeighted AveragesWeighted AveragesWindowsWindowsWorkflow ManagementWorkflow ManagementWormsWormsYield AnalyticsYield AnalyticsZ-ScoresZ-ScoresZ-TestsZ-Tests

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Data Analyst Levels

Intern Data Analyst

Intern Data Analyst

An Intern Data Analyst is a student or recent graduate who supports the data analysis efforts of an organization. They assist in data collection, cleaning, and basic analysis tasks, gaining practical experience in data analytics and contributing to the organization's data-driven decision-making process.

Graduate Data Analyst

Graduate Data Analyst

A Graduate Data Analyst brings fresh perspectives to data interpretation, utilizing foundational skills in statistics and data visualization to translate complex datasets into actionable insights. They are detail-oriented, analytically minded, and poised to support data-driven decision-making.

Junior Data Analyst

Junior Data Analyst

A Junior Data Analyst is an entry-level professional who supports data analysis activities within an organization. They assist in data collection, cleaning, and analysis, while also gaining exposure to various data analysis tools and techniques. Junior Data Analysts are eager to learn and contribute to the organization's data-driven decision-making process.

Data Analyst (Mid-Level)

Data Analyst (Mid-Level)

A Mid-Level Data Analyst plays a crucial role in transforming data into meaningful insights that drive business decisions. With a solid foundation in data analysis and strong technical skills, they collaborate with teams to gather and analyze data, create reports, and provide actionable recommendations. Their expertise helps organizations leverage data to optimize performance and achieve strategic goals.

Senior Data Analyst

Senior Data Analyst

A Senior Data Analyst is a seasoned professional who turns complex data into actionable insights that inform strategic decisions. They lead analytical projects, mentor junior analysts, and translate data trends to guide business strategies. Their expertise ensures that data narratives are clear and impactful for driving organizational success.

Lead Data Analyst

Lead Data Analyst

A Lead Data Analyst is a seasoned professional who not only possesses advanced data analysis skills but also provides strategic guidance and leadership to a team of analysts. They are responsible for overseeing complex analytical projects, driving data-driven decision-making, and ensuring the accuracy and integrity of data. With their expertise, they play a crucial role in shaping the organization's data strategy and driving business success.

What are the responsibilities & duties of a Data Analyst

The duties and responsibilities of a Data Analyst do vary from role to role, industry to industry, company to company. It also depends on how senior or junior the role is. That said, these are the typical responsibilities of a Data Analyst:

  • Collate, interpret and cleanse new datasets for new analyses & reports
  • Creating and maintaining insightful and simple to interpret reports, dashboards and other visualizations
  • Monitoring KPIs and proactively investigating any unusual trends
  • Encouraging a data-driven culture with the organisation
  • Creating and updating data models used in reports and dashboards
  • Creating and maintaining statistical models
  • Maintaining documentation for various data systems
  • Various ad hoc analytical projects, for example creating forecast or classification models
  • Ad hoc analysis into trends and ‘deep-dive’ analyses

Ultimately, the end goal is normally to generate ‘actionable insights’ - this is sometimes called the ‘so-what’ of the analysis. The analysis is considered valuable to the extent that it’s insightful (i.e. telling the audience something that they didn’t know) and ‘actionable’ (can the audience actually do anything with this insight, or is it just more of an academic curiosity).

What are the required skills & experiences of a Data Analyst?

Just like the responsibilities and duties, the required skills & experiences do vary from role to role. Here’s a typical set of requirements for a Data Analyst:

  • Strong experience in using SQL to write complex queries
  • Experience using Python or R to conduct data analysis
  • Experience creating & maintaining dashboards and reports in a BI tool, such as Tableau or PowerBI
  • Excellent written & verbal communication skills
  • Ability to work independently and also to collaborate with other teams
  • Experience communicating insights from complex & technical concepts to non-technical/non-data stakeholders
  • Strong business acumen
  • Experience in defining new metrics
  • Strong all round analytical ability including a high attention to detail, scepticism and common sense

What are some other titles Data Analysts may also be called?

Titling in analytics is sometimes a little blurry. What one company calls a data scientist, another may call a data analyst, for example. That said, there has been a bit more consistency in titles over the last few years as the data industry has matured.

Depending on the organization, a Data Analyst may also be referred to as an Insights Analyst or a Data & Reporting Analyst.

A title is just a title, and for candidates, we’d recommend reading the job description and asking the hiring manager for details of what you’ll actually be doing to understand the role.

For organizations, we’d recommend aligning your job titles with what the market generally uses. E.g. over-selling a basic reporting role as a Data Analyst will lead to disappointment among candidates, and cause you to attract the wrong types of candidates.

What's a typical day in the life of a data analyst?

A day-in-the-life of a Data Analyst of course would vary a lot from team to team and organization to organization. Each organization has different datasets, work at different cadences with different methodologies, and have different technology set-ups.

But as a general guide, here’s what you might expect in a typical day:

  • Start your day by checking existing dashboards for key metrics that you monitor.
  • You notice the data is missing completely. After checking a shared Slack channel, you see a message from your data platform team - the ELT process failed, so the dashboards won’t be updated for another 20 minutes. Coffee time!
  • Once the dashboards are up-to-date, you notice a negative trend in a core market - that’s something you’ll want to dig into further.
  • Checking your email, you see one ‘urgent’ request for access to an existing report. You ping your colleague a Slack message who controls access to dashboards and ask them to sort it out.
  • Time for the daily team meeting, where you run through what you did yesterday, what you’re doing today and what you’ll do tomorrow.
  • You start digging into the negative trend, just as the market manager messages you ‘What’s going on here do you think? Please investigate it.’ You press on, using various existing dashboards and writing some specific SQL to dig deeper.
  • After lunch you seem to have found one issue. Looks like performance took a hit on Android devices in the Korean market. Maybe an app update went wrong? You ask the product team if they released any big changes yesterday, but they also seem confused.
  • After a bit of back and forth, the product team seems to have discovered a bug has been introduced that’s caused the sharp decline. Well spotted!
  • You update a few people on what you’ve found and what’s going on.
  • With ad hoc analysis completed, you’ve got just enough time to return to a modelling project - you’ve been working on a new way to forecast revenue more accurately. Time for some Python!

What skills does a Data Analyst need?

The scope of a Data Analyst role does vary a little bit by the size of the company, the industry, and the data maturity of the organization. This then dictates the skills that are (or are not) needed to be a Data Analyst.

While generally companies don’t expect tool-specific experience, the caveat to this is contracting roles, that might only last for 3-12 months. For these roles, the expectation is that you can hit the ground running, and so the company would expect you to already have familiarity with their stack, which is fair enough.

For lower data maturity organizations, you should expect to see less well-established data ecosystems. This means most likely an incomprehensive data warehouse, probably with limited access tied up with an ‘IT team’, and a lot of analytics being done in Microsoft Excel.

That said, for a typical Data Analyst role, these skills would be considered mandatory:


SQL is typically considered a must-have for a Data Analyst. Note, there are various subtle differences in SQL syntax, depending on the relational database management system (RDMS) that an organization is using (e.g. SQL Server, MySQL, etc.). Normally companies don’t require experience in any specific one of these, just that you have experience in any of them, as they’re all very similar. The differences in SQL syntax is comparable to the difference between, say, American English and Australian English.

A visualisation tool

Data visualization skills are considered a must-have. Most commonly, this will be some experience in putting together dashboards and reports in something like Tableau, PowerBI, Looker etc. Companies are looking not just for experience in the mechanics of creating & maintaining visualizations, but also the general ability to communicate graphically. This is because the goal of analytics is to influence decisions, with visualization being a critical output of the analysis.

Python or R

Python and R are the most commonly used programming languages as a Data Analyst.

Companies are generally agnostic as to which one a candidate would know well, as long as they know one of them. If you are a candidate and have to choose which to learn from scratch, it would probably be best to go for Python. It’s a general-purpose programming language so you can use it to do pretty much anything you like, while R is really made specifically for statistics and data science. The Python vs R debate is not something we’ll get into, but for what it’s worth, around 80% of candidates when taking tests on Alooba choose Python over R, when given the choice. The popularity has increased consistently over the last 5 years.

Analytical skills

Surprise, surprise - a Data Analyst needs to have analytical skills. Included in here is a very keen attention to detail for all types of data. Your stakeholders will be relying on you to scrutinize the data with extreme prejudice and a healthy dose of skepticism. You ultimately have to arrive at the right answer through correct analysis and good assumptions and deal with the ambiguity involved in real-life analysis.

Common sense is also a big part of this - for example, if you check your dashboard in the morning and it shows that there have been no sales at all in the last 12 hours, before you go blasting the ‘All Staff’ mailing list telling everyone the company is dying, you’ll probably want to check that the data warehouse ETL process completed successfully (hint: the data probably just is not up-to-date).

The line between general analytical skills and what’s termed ‘data literacy’ is perhaps a little blurred. Data literacy would include being able to know when to use a median vs a mean, not to take an average of an average (please, just don’t), and why putting time on the X-axis of a line chart probably isn’t a great idea.

Communication skills

The goal of any data analysis is to influence a better decision to be made. As a Data Analyst, you could create the most amazing analysis of all time, but it will all be wasted if you aren’t able to easily describe the key takeaways from the analysis. You will typically need to communicate these to people with less technical skills than yourself, who are less familiar with the data than you are. They’ll often be more senior, time-poor, and might not want to get into the nitty-gritty details. Data Analysts need to be able to explain the ‘so-what’ of their analysis quickly & easily. This could be through visualizations, a written email, a presentation, or simply an informal chat.

Business Acumen

Data Analysts are typically working in - or very close to - ‘the business’. Unlike Data Engineer roles that can be a little more abstract and disconnected, Data Analysts are right in the thick of it. There’s definitely an expectation that they’d be able to understand the key drivers of business success, understand what metrics should be tracked and why, understand underlying trends, interpret bigger-picture macro trends, etc. Maybe the best way to describe this skill is that they need to be able to think and act like an owner.

What are nice-to-have skills for a Data Analyst?

In addition to the above need-to-have skills for a Data Analyst, there’s also a lot of nice-to-have skills. Generally speaking, the smaller the organization you operate in, the wider the scope will be of your role, and so the more skills you’d be expected to have (or pick up).

Data modeling

Data modeling is basically the work of designing & maintaining data marts, datasets, views, etc. While data modeling may ultimately be reduced to some SQL, the modeling itself is distinct from this.

For larger organizations, data analysts are unlikely to be involved in building or maintaining datasets in the data warehouse. This would more commonly fall on a data engineer, business intelligence developer, or SQL developer. Nonetheless, the skillset is useful, especially as you will want to communicate with the person making a change to a data model and understand how it works.

E.g. Imagine you have a Tableau dashboard, and you’d like to create a new visualization that involves a new column not currently in your dataset. You’ll need to add this from the source, and understanding how the dataset is built will help you understand how this needs to be done.

Machine learning

Data analyst roles will often have machine learning - or in any case some kind of more advanced statistics - as a nice to have. If there is an expectation, it would normally be entry-level machine learning, such as regression models (linear or logistical), and some entry-level classification work. The expectation would be that you know how to implement, build & interpret basic models using some relevant package in R or Python. For the most part, though, the machine learning work would be done by data scientists, not data analysts.

Which tools and technologies do Data Analysts use most?

Data Analysts will typically use these technologies on a daily basis:

  • Data warehouse or lake: Professionally organized datasets would normally be available within a data warehouse or data lake for a Data Analyst to then conduct analysis on. If it’s a data warehouse, they’ll normally query this database using the language SQL.
  • Visualization tool: A visualization tool allows Data Analysts to create compelling visualizations to tell an easy-to-interpret story about their analysis.
  • Programming language: Languages like Python & R are commonly used by Data Analysts to conduct their analysis.
  • Documentation (email, Slack, wiki, PowerPoint): Ultimately Data Analysts will need to share the final output of their analysis somehow, either via email, Slack, some kind of internal wiki, or a presentation tool.

There’s some crossover in each of these technologies in the analytics process. For example, a Data Analyst might do all their data wrangling in SQL, and then pipe the data into a Python environment for their analysis and visualization.

Frequently Asked Questions

Some organizations ‘embed’ their Data Analysts within functions, while others operate as more of a centralized team as a ‘shared service’. If Data Analysts are embedded into specific functions and teams, their titles are often slightly different to reflect that. For example, a Data Analyst within the risk management team of a bank would often be called a Risk Analyst. These days, in larger organizations, almost all functions have Data Analysts helping them make better decisions. The biggest volumes of Data Analysts are normally in marketing, product, sales, operations, commercial, risk & HR.

A Data Analyst and a Product Analyst are quite similar in many ways. Really, a Product Analyst is just a special type of Data Analyst. Product Analysts are basically Data Analysts who work on a…product. Shock, horror.

These roles normally exist in companies where the product is the company - ‘product companies’ (tech companies). These Product Analysts will have a skillset similar to a Data Analyst in any other industry, but with the added expectation that they will have experience in customer analytics, user behavior analysis, understanding how data is tracked on the web, and experimentation (A/B testing).

Product Analysts often work in a cross-functional team - sometimes called a ‘squad’ - that is composed of software engineers, designers, a product manager, and potentially a scrum master. This team works collectively to build the product, and the idea is that the team is a self-contained, self-organizing unit with all the skills needed to execute that specific product.

Typically not. A Data Analyst will normally be an ‘individual contributor’ role in most organizations. A Senior Data Analyst or Lead Data Analyst might have some light managerial duties (maybe 1-2 direct reports), but will more often still be an individual contributor. You will normally start managing people at the Manager of Data Analytics level and beyond.

Who you report to does depend on the size & data maturity of the organization that you are in. For large organizations in industries that are reasonably data mature, like banking, finance, retail & tech, you would normally expect to report to a Senior Manager of Analytics, Head of Analytics, or Director of Analytics. In smaller organizations where there is not an established data team at all, you might report to someone heading up tech. If you are a Data Analyst within a function (e.g. marketing, HR, operations), then you would expect to report to people managing those functions (e.g. Head of Marketing).

Depending on the size of the organization, there might be several levels of Data Analyst, starting with Graduate or Junior Data Analyst, Data Analyst, Senior Data Analyst, and Lead Data Analyst. Beyond that, the roles are no longer purely individual contributor roles and instead become managerial, at least in part.

Data Analysts will collaborate with other people in the data team, such as their manager (e.g. a Head of Data Analytics), Data Engineers & Data Scientists. There will also be the Data Analysts' end-user/audience, which will typically be managers of other teams. For example, if a Data Analyst works embedded into a marketing team, then their audience will often be the Head of Marketing and other marketing managers. Depending on the size of the organization, the audience might be even more senior, such as the C-Suite or company founders.

Data Analysts sometimes also work with external organizations, but this is less common. For example, a Data Analyst in marketing might work with an external marketing agency, sharing data for them to optimize their marketing campaigns.

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I was at WooliesX (Woolworths) and we used Alooba and it was a highly positive experience. We had a large number of candidates. At WooliesX, previously we were quite dependent on the designed test from the team leads. That was quite a manual process. We realised it would take too much time from us. The time saving is great. Even spending 15 minutes per candidate with a manual test would be huge - hours per week, but with Alooba we just see the numbers immediately.

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