Turning organizational data into decisions and insight.
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 interpretation-first rather than infrastructure-first, sitting closer to business teams than to engineering. Practitioners often come from non-CS backgrounds and rely on visualization and storytelling alongside technical skill. 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 tooling, where Power BI is the default reporting surface. The largest BI specialization by hiring volume.
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 consolidation.
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 tooling. Heavier on the data platform side than pure analytics, with focus on querying and modeling for reporting rather than pipeline construction. The smallest but fastest-growing analytics segment.
Data analytics and BI hiring breaks into a SQL-and-language core that defines the role and three platform tracks that shape it depending 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 anchor the querying-and-scripting prerequisite that drives daily analyst work, ad-hoc analysis, and advanced analytics. Excel, Google Sheets, VBA, and Apps Script anchor the spreadsheet and workflow-automation side that powers business reporting. Data Visualization, Report Generation, and Dashboarding define the output side of the job, surfaced through whichever BI platform the team has standardized on. ETL and Data Transformation describe how analysts shape source data before it reaches a model or dashboard, while Exploratory Data Analysis and Data Interpretation cover the analytic layer where insight is drawn. The three tracks split by platform allegiance: Power BI with DAX and Power Query for Microsoft shops, Tableau for non-Microsoft BI estates, and Azure Synapse, Databricks, and Snowflake where cloud data warehouses anchor the reporting layer.
querying, scripting & advanced analytics
business reporting & workflow automation
Data Warehousing, Data Governance, and Data Quality describe the governance layer that sits over the warehouse, naming the discipline rather than a specific tool. Azure Data Factory and AWS Glue handle pipeline orchestration where analysts own the ingest. Azure DevOps and Git surface in shops that version their reports and pipelines as code, bringing analyst work closer to engineering practice. The auxiliary surface here stays compact and oriented around governance and the operational scaffolding that turns analyst output into something a team can rely on week over week.
Data Analytics and BI is the lightest-volume profile in the snapshot, running at ~20 per week across the window. The mix is WITCH-dominant, with Indian IT services and WITCH at ~65% and MNCs and GCCs at ~17%. Median pay: mid at 29 LPA, senior at 52 LPA. The staff band is too small to surface a quartile this snapshot. The panels below cover volume and company mix, then a zoom into fresher-accessible roles.
Postings per week, segmented by company class:
Window overall · ~20 / wk
Volume runs the lightest in the snapshot: Jan averaged ~18 per week, dipping to ~7 in February before recovering through Mar and Apr to ~25 to ~30 per week, then settling near ~18 in May. The mix is WITCH-dominant with the snapshot's highest WITCH share at ~65%, narrowing through the window from ~58% in January to ~47% in May. The MNC rotation runs in parallel: MNCs and GCCs climbed from ~19% in January to ~30% by May, a gain of ~11 pp. Unicorns and Indian product, MAANG and elite global tech, Funded startups, and Established SME each sit in the lower end of their cross-profile range here, with multiple flags marking this as the lowest-share profile across those categories.
Postings per week, segmented by experience:
Window overall · ~20 / wk
The experience mix tilts heavily to Mid at ~74% window-overall, the highest Mid share in the snapshot. Senior holds ~19% and FA ~6%, with Staff at ~1%. Across the window, the Mid concentration grows from ~62% in January to ~83% in Feb before settling back to ~71% in May. FA share stays in a narrow ~5 to ~10% band across populated months, placing Data Analytics and BI in fresher-tight territory.
Data Analytics and BI is a fresher-tight profile. Fresher-accessible here means roles open to ENTRY and JUNIOR LEVEL applicants, which make up ~6% of all postings on this profile and run at ~0 to 6 per week across the weekly buckets. Inside the fresher cut, Indian IT services and WITCH sit at ~31%, down from ~65% in the overall mix.
Inside the fresher cut · company class distribution
In the FA cut, Indian IT Services / WITCH leads at ~31% (vs ~65% in the overall mix). Versus overall, Indian IT Services / WITCH drops 34pp to ~31%. On the other side, Other rises 16pp to ~19% and MNCs & GCCs rises 8pp to ~25%.
Entry-level pay distribution (LPA)
Mass anchors at 12 LPA (~33% of FA offers), followed by 4 LPA at ~22% and 18 LPA at ~22%; the distribution is mid-anchored. The 30+ LPA tail stays absent because MAANG and elite global tech presence at FA is only negligible. The 20 LPA rung at ~11% tracks Unicorns at ~3% plus funded startups at ~6%. The 4 to 8 LPA entry mass at ~33% traces to Indian IT services at ~31% and Lala at ~6%.
Data analytics & BI shows a markedly fresher- and mid-skewed ladder, with Senior+Staff share running well below the snapshot baseline. Salary samples thin out at the entry and staff ends, so the IC premium can only be read at the Mid and Senior rungs. Pivot paths into adjacent profiles are narrow with a clear leaning toward data engineering and data science. The MNC / GCC tier hires at a meaningful ~30% senior share with a ~53% senior pay premium, making it the realistic aspirational employer-tier target for this profile. The four panels below answer the four questions most candidates ask: is the ladder real, does expertise pay, where can I pivot if I want out, and how do I get to the premium employer tier.
Distribution of postings by seniority level (this profile vs the snapshot baseline of all 15 profiles, same window):
The ladder runs well below baseline at the senior end. Mid dominates at ~73% versus the ~54% baseline, while Senior+Staff at ~21% sits roughly 16 percentage points under the ~37% baseline, with Staff specifically at ~1% versus a ~6% baseline. Fresher hiring at ~6% also runs slightly below baseline. The shape is consistent with a profile where most hiring happens for analyst-level execution work and where the rung beyond Senior is rare in the open market. Verdict: not a dead-end at the Senior level, but a thin Staff bench means deep IC progression past Senior happens largely outside the public posting pool.
Closest profiles by SkillSet-level overlap (Jaccard similarity over the SkillSets cited in at least 10% of postings for each profile, same window). New SkillSets required is the count of SkillSets that appear in the adjacent profile's set but not in this profile's:
DATA_ENGINEERING
~25%
6 shared · ~13 new required
Shared core skillsets
New skillsets required (examples)
DATA_SCIENCE_AND_ML
~21%
4 shared · ~8 new required
Shared core skillsets
New skillsets required (examples)
GENERALIST_SWE
~17%
3 shared · ~7 new required
Shared core skillsets
New skillsets required (examples)
AI_AND_LLM
~12%
3 shared · ~14 new required
Shared core skillsets
New skillsets required (examples)
DEVOPS_AND_PLATFORM
~12%
3 shared · ~14 new required
Shared core skillsets
New skillsets required (examples)
Pivot paths are narrow. The two closest profiles, Data Engineering (~25%) and Data Science & ML (~21%), share Relational Databases, Programming Languages, Python for Data Science, and Cloud Data Warehouses but require many new SkillSets to ramp: Data Engineering adds 13 new SkillSets including Spark, ETL Orchestration, Cloud Platforms, and Containers, while Data Science & ML adds 8 more spanning deep learning frameworks and MLOps. Beyond the top two, every adjacency falls below ~17%, including Generalist SWE and AI & LLM Applications. Verdict: horizontal mobility is real but never cheap, with the cleanest route being a deliberate ramp into data engineering rather than a sideways step.
MNCs and GCCs share of postings within this profile, broken out by seniority level:
Share by seniority
Senior pay · same profile
Skills that distinguish MNC / GCC senior postings
The MNC / GCC tier shows a senior-skewed shape in data analytics & BI, with shares running ~25% at FA, ~12% at Mid, and ~30% at Senior. The Staff cohort is too thin to evaluate. The Mid trough reflects mid-level analyst hiring concentrating in Indian IT services where service-line BI work dominates, while MNCs lean toward fresher pipelines and senior analytics specialists. MNC senior median sits at ~58 LPA versus ~38 LPA for the rest of the field, a ~20 LPA / ~53% premium, smaller than other D-tier profiles but still material. The skills that distinguish MNC senior analyst postings emphasise engineering-flavoured data work over traditional BI tooling: Report Generation (+30pp), Python (+30pp), and SQL (+22pp) lead, with ETL Processes (+22pp) and Data Pipelines (+19pp) close behind. Google BigQuery, Cloud Looker, PySpark, and Snowflake each appear in ~19% of MNC senior postings but are effectively absent in the IT-services comparison. Verdict: MNCs and GCCs are the realistic aspirational tier; build Python and ETL skills on the SQL+BI foundation and adopt a cloud data warehouse (BigQuery or Snowflake) to compete for the senior rung.