Tools, Frameworks & Implementation Proficiency Topics
Practical proficiency with industry-standard tools and frameworks including project management (Jira, Azure DevOps), productivity tools (Excel, spreadsheet analysis), development tools and environments, and framework setup. Focuses on hands-on tool expertise, configuration, best practices, and optimization rather than conceptual knowledge. Complements technical categories by addressing implementation tooling.
Spreadsheet Analysis and Modeling
Hands on skills for analyzing, modeling, and reporting data using spreadsheet software and lightweight tabular tools. Candidates should demonstrate data organization and cleaning techniques, proficiency with formulas and functions for calculations and conditional logic, and use of lookup and aggregation methods. Expect fluency with pivot tables for summarization and segmentation, charting and other visualizations, and building simple dashboards and reports. Important skills include correct use of absolute and relative references, efficient spreadsheet layout for accuracy and collaboration, conditional formatting, and strategies for working with large datasets. Candidates may also be expected to perform basic statistical measures such as averages medians and distribution checks, compute growth and conversion metrics, and automate repetitive tasks using built in scripting or macro features. Interviewers frequently assess the ability to derive actionable insights from tabular data quickly and accurately, often under time constraints.
Hands On Projects and Problem Solving
Discussion of practical projects and side work you have built or contributed to across domains. Candidates should be prepared to explain their role, architecture and design decisions, services and libraries chosen, alternatives considered, trade offs made, challenges encountered, debugging and troubleshooting approaches, performance optimization, testing strategies, and lessons learned. This includes independent side projects, security labs and capture the flag practice, bug bounty work, coursework projects, and other hands on exercises. Interviewers may probe for how you identified requirements, prioritized tasks, collaborated with others, measured impact, and what you would do differently in hindsight.
Business Intelligence Tool Proficiency
Covers knowledge and hands on skills using enterprise business intelligence tools such as Power BI and Tableau. Candidates should demonstrate the end to end workflow: connecting to diverse data sources including spreadsheets, relational databases, data warehouses, and cloud services; exploring and profiling data to understand schema and quality; and performing data transformation and cleaning using extract transform load processes or built in tool features. Includes building efficient data models with appropriate relationships, hierarchies, and performance minded design, and understanding when to use extracts versus live connections and aggregation strategies. Candidates should be able to create visualizations and interactive dashboards by mapping fields to charts, selecting appropriate chart types, applying filters and parameters, configuring drill down and drill through interactions, and assembling visuals into coherent reports. Covers calculated fields and custom metric creation using expression languages such as Data Analysis Expressions and Tableau table calculations, and awareness of performance implications of complex calculations. Also includes familiarity with differences between paginated reports and interactive dashboards, publishing and sharing workflows, deployment and distribution strategies, governance and access controls including row level security and workspace organization, versioning and refresh scheduling, and basic troubleshooting and optimization techniques. Candidates should be prepared to discuss real projects where they chose visualizations, resolved data quality or performance challenges, iterated on stakeholder feedback, and measured adoption and business impact.
Date and Time Operations
Tests practical skills for working with dates and times in data, reporting, and everyday technical work. Candidates should be comfortable with date and time data types (date vs. timestamp vs. timestamp with time zone) and their storage and comparison semantics, date filtering, relative date ranges such as last-n-days or rolling windows, inclusive versus exclusive range boundaries, timezone conversions and daylight saving time edge cases, business-day and holiday-aware calculations, epoch/unix timestamp conversions, and fiscal or custom period logic. Interviewers assess the ability to translate a reporting or business requirement into correct date logic, choose the right date/time representation for a given system, and reason through common pitfalls such as timezone mismatches between systems and off-by-one boundary errors. This shows up across contexts: SQL queries, spreadsheet formulas, BI tool calculated fields and filters, and date/time handling in general-purpose code.
Power BI Advanced Features and DAX
Focuses on advanced Power BI capabilities and deep proficiency in the DAX expression language and data modeling specific to Power BI. Includes writing and optimizing DAX measures and calculated columns using functions such as CALCULATE, FILTER, SUMX, and advanced time intelligence functions like DATEADD and year to date patterns. Covers data model optimization principles including star schema design, relationship management, and reducing cardinality for the VertiPaq engine. Addresses query folding, DirectQuery versus Import mode trade offs, incremental refresh configuration, row level security, query diagnostics and performance profiling, and techniques to tune DAX and model performance for large datasets.
Advanced Excel and Google Sheets
Covers advanced spreadsheet skills used for data analysis, reporting, and ad hoc business intelligence work in both Microsoft Excel and Google Sheets. Core capabilities include lookup and reference functions such as VLOOKUP and INDEX MATCH, aggregation and conditional functions such as SUMIF and AVERAGEIF, logical functions such as IF, array formulas, and nested formulas. Candidates should be comfortable building and manipulating pivot tables to summarize data, using conditional formatting and data validation to ensure data quality, and structuring worksheets with named ranges and proper use of absolute versus relative cell references. The topic also includes creating dynamic formulas and simple dashboards for visualization, charting best practices, data cleaning techniques, and performance considerations for large worksheets. At an advanced level, familiarity with automation and workflow improvements such as macros or scripts, query and transform capabilities, and how spreadsheets integrate or compare with business intelligence tools is expected.
Basic SQL Selection and Filtering
Foundational skills for retrieving and filtering data using SQL. Covers writing SELECT statements to choose columns, using WHERE clauses to filter rows with comparison operators, combining conditions with AND and OR, using NOT, pattern matching with LIKE, set membership with IN, range filters with BETWEEN, handling NULL values with IS NULL and IS NOT NULL, and basic boolean logic. Candidates should be able to write correct queries to answer simple business questions, explain why a filter returns no rows, and identify common syntax errors in simple queries.
Reading and Understanding Data Schemas
Be able to look at a data schema (table structure) and understand what data is available and how tables relate. At the start of the assessment, you'll be given a schema. Spend a minute understanding it before writing queries. Understand primary keys, foreign keys, and data types.
Technical Tools and Competency
Assess the candidates practical experience with business intelligence and operational tools, their depth of proficiency, and their ability to learn and apply new systems. Topics to cover include which business intelligence platforms they have used such as Power BI, Tableau, and Looker, the duration and level of hands on experience with each, specific projects where they built dashboards or reports, and the candidates role in data modeling and visualization. Also include familiarity with general operational tools such as spreadsheet software, analytics platforms, project management systems, human resources information systems, and other domain specific software. Candidates should be ready to explain tool selection, how they integrated data sources, any involvement in implementation or configuration, examples of key metrics and dashboards they built, and how they troubleshoot or improve existing reports. For junior level candidates, emphasize practical skills such as creating dashboards, designing reports, basic data modeling, cleaning and preparing data, and demonstrating learning agility for company specific systems. For mid and senior levels, assess deeper topics such as automating extract transform load processes, optimizing data models, writing structured query language queries or scripts for data transformation, governance and sharing practices, and mentoring others on tool usage.