Can Your SQL and Analysis Portfolio Pass a Technical Screening?
Data analyst roles at Indian startups and consulting firms test SQL window functions, BI tool proficiency, and the ability to translate business questions into queries. We audit your SQL repositories and analysis notebooks against the exact criteria these companies test in technical screenings.
What Data Analyst Interviews Actually Test
Entry-level data analyst roles in India sit at an intersection of skills that most candidates prepare for in isolation: SQL beyond JOINs (window functions, CTEs, query optimization), BI tool proficiency (Power BI or Tableau), Python for data manipulation (pandas, numpy), and business communication (translating stakeholder questions into analytical queries). Candidates who prepare only SQL fail the Python round. Candidates who prepare only Python fail the SQL round. Candidates who prepare both but cannot explain their analysis to a non-technical stakeholder fail the communication round. Our audit evaluates your portfolio across all four dimensions and identifies the specific skill gaps that block you at each interview stage.
SQL proficiency for analyst roles is evaluated at a higher level than for backend developer roles. Backend developers are tested on JOINs, GROUP BY, and basic optimization. Data analysts are tested on window functions (ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD), CTEs and recursive queries, complex aggregations with multiple grouping levels, and the ability to write a query that answers a business question asked in plain English. Our audit includes a set of 10 business-scenario SQL problems that mirror the exact question patterns used in analyst technical screenings at Indian startups and consulting firms.
The portfolio dimension that most analyst candidates neglect is the end-to-end analysis pipeline: raw data → cleaning → transformation → analysis → visualization → business recommendation. A candidate who can show one complete pipeline — from a messy CSV to a dashboard with actionable insights — demonstrates more analytical maturity than a candidate with 10 half-finished Jupyter notebooks. Our audit identifies whether your portfolio demonstrates this pipeline or consists of fragmented analysis exercises.
System Comparison
| EVALUATION CRITERIA | TYPICAL ANALYST PORTFOLIO | ANVIL ANALYST DIAGNOSTIC |
|---|---|---|
| SQL Proficiency | SELECT with JOINs. No window functions. No CTEs. Queries timeout on datasets larger than 1,000 rows. | Window function proficiency tested. CTE and subquery evaluation. Query execution plan analysis. 10 business-scenario SQL problems calibrated to interview difficulty. |
| Analysis Pipeline | Disconnected Jupyter notebooks. Raw data imported but never cleaned. No documentation of methodology. | End-to-end pipeline audit: data ingestion, cleaning, transformation, analysis, visualization, business recommendation. Completeness scored per stage. |
| BI Tool Proficiency | Static charts without interactivity. No drill-down. No filters. 'Dashboard' is a screenshot, not a live Power BI/Tableau file. | Dashboard interactivity audit. Filter/slicer configuration. Drill-through design. Data refresh configuration. Visual best-practices checklist. |
| Business Communication | Analysis with no recommendations. 'The average is 42' with no context on whether that is good or bad. | Narrative quality audit. Are findings actionable? Is methodology explained? Can a non-technical stakeholder understand the output? |
Frequently Asked Questions
Do I need both Power BI and Tableau, or is one sufficient?
One is sufficient. Proficiency in one BI tool with demonstrated dashboard interactivity (filters, drill-down, cross-filtering) signals transferable BI skills. The tool itself is secondary to the analytical thinking demonstrated through the dashboard. Our audit does not penalize you for choosing one tool over another. We evaluate the quality of analysis communicated through the tool you chose.
What if my analysis projects use Excel instead of Python?
Excel proficiency is a legitimate analyst skill, especially at consulting firms and mid-size companies. Our audit adapts to your toolset. We evaluate whether your analysis is correct, your methodology is documented, and your conclusions are supported by the data — regardless of whether you used Python, Excel, or both. However, for roles at tech startups and product companies, Python proficiency is increasingly expected. We flag this gap if our audit suggests you are targeting companies where Python is a requirement.
Audit Your Data Analyst Portfolio
Submit your GitHub, Tableau Public/Power BI links, and analysis notebooks. Our data team evaluates your SQL, Python, and visualization skills against the criteria that Indian startups and consulting firms test. Receive a 24-hour diagnostic report with remediation priorities and SQL practice recommendations.
- Expert-verified in 24 hours
- Actionable learning paths