Profile
Turning organizational data into decisions and insights.
Data analysts query data and build reports, dashboards, and business insights using SQL, Excel, and BI platforms like Power BI, Tableau, and Looker. The role is about interpreting data rather than building infrastructure, and it sits closer to business teams than to engineering. Many analysts come from non-CS backgrounds and rely on visualization and storytelling alongside technical skill. It is distinct from data engineering, which builds the systems analysts query.
Roles centered on Power BI, DAX, Power Query, and the broader Microsoft BI stack including SSRS, SSAS, and Azure Analysis Services. Often found in organizations standardized on Microsoft tools, where Power BI is the default reporting surface.
Roles focused on Oracle BI Publisher, OBIEE, Oracle Analytics Cloud, and Oracle EPM and Planning. Typically found in large enterprises with Oracle infrastructure, where BI sits alongside financial planning workflows. A specialist track tied to a specific vendor ecosystem.
Roles centered on Tableau, Qlik, SAP BI, Looker, MicroStrategy, or other non-Microsoft and non-Oracle BI tools. Platform expertise varies but the core work remains visualization and business reporting. Common in organizations that standardized on a specific vendor before Power BI became the default.
Roles where the core work revolves around SQL, relational databases, stored procedures, and query optimization, with BI as a reporting layer. T-SQL and PL/SQL are common dialects. The platform-agnostic analytics path, often inside organizations with custom or older reporting infrastructure.
Roles combining cloud data warehouses such as Snowflake, Redshift, BigQuery, Synapse, and Databricks with BI tools. Heavier on the data platform side than pure analytics, with the focus on querying and modeling for reporting rather than pipeline construction.
Data analytics and BI hiring requirements mainly ask for a SQL and language core in addition to three platform tracks. The track depends on whether the team standardizes on Power BI, other BI vendors, or a cloud warehouse stack. The two subsections below separate what hiring managers expect from what they value as a plus.
SQL, Python, and R are the must-have skills for querying data and scripting, used for analyst work, one-off analysis, and deeper analytics. Excel, Google Sheets, VBA, and Apps Script cover the spreadsheet and workflow-automation side, used for business reporting. Data Visualization, Report Generation, and Dashboarding are the output, the part people actually see, built in whichever business-intelligence (BI) tool the team has settled on. ETL and Data Transformation cover cleaning and reshaping the source data before it reaches a model or dashboard, while Exploratory Data Analysis and Data Interpretation cover the analysis itself, where the findings come from. The work then divides into three areas by platform. Power BI with DAX and Power Query is used in Microsoft shops, and Tableau in non-Microsoft setups. Azure Synapse, Databricks, and Snowflake come in when the data sits in a cloud data warehouse that feeds the reporting.
querying, scripting, and advanced analytics
business reporting and workflow automation
Data Warehousing, Data Governance, and Data Quality are about the rules and care taken over the data warehouse, which is the central store the company's data flows into. These name an area of work, not a single tool. Azure Data Factory and AWS Glue run the data pipelines when the analysts are the ones bringing the data in. Azure DevOps and Git show up in teams that track every change to their reports and pipelines, the way software engineers track changes to code. This moves analyst work closer to how engineers work. There are only a few supporting skills here, and they are mostly about governance and the steady, day-to-day work that makes an analyst's output something the team can rely on week after week.
Data Analytics and BI runs in the upper-middle tier, fifth by volume, with around 200 postings a week. The mix is overwhelmingly Indian IT Services and the WITCH firms at around two-thirds, one of the highest single-category concentrations of all the profiles. The sections below lay out weekly volume and the company mix, then turn to the roles open to freshers.
Postings per week, segmented by company class:
Window overall · ~190 / wk
This profile is led by the WITCH firms, which sit near two-thirds of the mix. Weekly demand has been up and down rather than trending. Indian IT Services and the WITCH firms tightened their grip over the period, moving from around two in five early on to around two-thirds at the latest week. The Other category collapsed from around two in five to a tiny slice over the same stretch. That concentration is the headline. Apart from Enterprise Platforms, few profiles lean this hard on a single category, which makes this profile the clearest read on service-firm demand.
Postings per week, segmented by experience:
Window overall · ~190 / wk
Mid-level roles make up the bulk at around three-quarters, one of the most mid-heavy splits of all the profiles. Senior trails well behind at just under a fifth, with fresher at well under a tenth and staff barely present. The shape stays flat from week to week, marking this as a profile built on experienced work rather than entry or staff hiring.
Roles open to freshers, meaning entry and junior level applicants, make up well under a tenth of Data Analytics and BI postings, toward the leaner end of the pack. Weekly fresher volume ranges widely from around 0 to 60 a week, either very few or very many depending on the week. Within the fresher roles, Indian IT Services and the WITCH firms loosen their hold a lot while smaller employers fill the gap.
Inside the fresher cut · company class distribution
Indian IT Services and the WITCH firms still lead the fresher roles at just under half, but that is far below their overall share, one of the largest drops in the mix. The Other category rises sharply and Funded Startups climbs a little. The fresher roles therefore spread across smaller and harder-to-place employers, rather than the IT services firms that lead most of the broader hiring.
Data Analytics and BI has one of the thinnest paths up to senior roles, with Senior and Staff together sitting far below the typical level across profiles. Switches are narrow, with Data Engineering the only nearby move and most other profiles a real stretch. Roles concentrate at Mid and rarely advance to Staff, and that missing top end is the most distinctive feature. The sections below cover whether the climb to senior is real, which sideways moves are within reach, and how to reach the top firms.
Distribution of postings by seniority level (this profile vs the rest of the market, the other 14 profiles, all-time):
Share of postings by band. Bars compare this profile against rest of market. Values approximate.
Mid makes up most roles at around seven in ten, far above the usual just-over-half. Senior trails at around a fifth against the usual three in ten, and Staff barely registers. Senior and Staff together sit far below the typical level, with most roles piled into the middle. Overall, the ladder flattens out after the mid level, with the senior steps largely missing.
Closest profiles by skill-set overlap, measured over the skill sets cited in at least one in ten postings for each profile in the same window. New skill sets required counts the skill sets that appear in the adjacent profile's set but not in this profile's:
DATA_ENGINEERING
~25%
5 shared · ~12 new required
Shared core skillsets
New skillsets required
DATA_SCIENCE_AND_ML
~20%
3 shared · ~7 new required
Shared core skillsets
New skillsets required
GENERALIST_SWE
~15%
2 shared · ~6 new required
Shared core skillsets
New skillsets required
AI_AND_LLM
~8%
2 shared · ~14 new required
Shared core skillsets
New skillsets required
DEVOPS_AND_PLATFORM
~8%
2 shared · ~15 new required
Shared core skillsets
New skillsets required
The one realistic move is Data Engineering, the most similar role, sharing the cloud warehouse, Python, and ETL core. It still asks for around a dozen new skill sets. Data Science and ML is a moderate reach on shared analytics languages but wants deep-learning and Spark skills. Beyond those, Generalist, AI and LLM, and DevOps are all far off, each sharing only a couple of skill sets. Overall, there is little scope to move sideways, with Data Engineering the single sensible step and everything else a major retraining.
MNCs and GCCs share of postings within this profile, broken out by seniority level:
Share by seniority
Skills that distinguish MNC / GCC senior postings
MNC and GCC hiring here is uneven across levels, around a fifth at fresher level, around a tenth at Mid, and around three in ten at Senior, with too few Staff postings to read. The senior spike shows MNCs and GCCs filling lead analytics roles rather than training juniors into them. The skills that set senior roles apart are Looker, Snowflake, BigQuery, and data governance. Overall, the MNCs and GCCs are mainly a senior-entry route here, so build cloud-warehouse and governance skills to step up.