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.
Aggregation Functions and Group By
Fundamentals of aggregation in Structured Query Language covering aggregate functions such as COUNT, SUM, AVG, MIN, and MAX and how to use them to calculate totals, averages, minima, maxima, and row counts. Includes mastery of the GROUP BY clause to group rows by one or more dimensions such as customer, product, region, or time period, and producing metrics like total revenue by month, average order value by product, or count of transactions by date. Covers the HAVING clause for filtering aggregated groups and explains how it differs from WHERE, which filters rows before aggregation. Also addresses related topics commonly tested in interviews and practical problems: grouping by multiple columns, grouping on expressions and date truncation, using DISTINCT inside aggregates, handling NULL values, ordering and limiting grouped results, using aggregates in subqueries or derived tables, and basic performance considerations when aggregating large datasets. Practice examples include calculating monthly revenue, finding customers with more than a threshold number of orders, and identifying top products by sales.
General Technical Tool Proficiency
Familiarity and practical experience with technical productivity and analysis tools such as SQL, Python or R, data visualization platforms like Tableau and Power BI, Excel, and statistical or analytical software. Candidates should be able to describe depth of expertise, typical use cases, examples of real world applications, automation or scripting practices, and how they select tools for different problems. This topic includes discussing reproducible workflows, data preparation and cleaning, visualization best practices, and integration of tools into cross functional projects.
Scikit Learn, Pandas, and NumPy Usage
Practical proficiency with these core libraries. Pandas: DataFrames, data manipulation, handling missing values. NumPy: arrays, vectorized operations, mathematical functions. Scikit-learn: preprocessing, model fitting, evaluation metrics, pipelines. Knowing standard patterns and APIs. Writing efficient, readable code using these libraries.
Business Intelligence Tools and Features
Covers expert proficiency with major business intelligence tools such as Tableau, Power BI, and Looker, and the advanced capabilities these platforms provide. Topics include creating calculated fields and parameters, conditional formatting, complex filtering, dashboard interactivity and responsive layout design, and best practices for visualization and user experience. Includes performance optimization techniques such as extract versus live connection trade offs, query optimization, incremental refresh strategies, and general performance tuning. Also covers governance and security features including access controls and sharing models, considerations for tool selection and recommending the right tool for a specific use case, and high level migration strategies between BI platforms.
Analytical Modeling and Documentation
Design and document analytical models and spreadsheets so they are auditable, maintainable, and easy for others to review and update. Core practices include structuring workbooks with a dedicated assumptions or inputs section, clearly separating raw data, detailed calculations, and summary outputs or key performance indicators, and applying consistent formatting, headers, and naming conventions. Avoid hard coded numbers by centralizing inputs, using named ranges and descriptive cell references, and documenting complex formulas with cell comments or explanatory notes. Maintain a documentation or readme sheet that explains model purpose, layout, assumptions, how to update inputs, and known limitations. Build validation checks and error flags, modularize logic for reuse, and design for scalability across larger data sets or additional time periods. Be prepared to explain sensitivities and scenario analysis, demonstrate how the model supports audit and review, and describe processes for versioning and change tracking.
Power BI Data Modeling & DAX
Understand Power BI's data modeling concepts: relationships (one-to-many, many-to-many), cardinality, filter propagation, and bidirectional relationships. Write DAX formulas for measures and calculated columns, including basic (SUM, COUNT, AVERAGE) and advanced functions (CALCULATE, FILTER, ALL, VALUES). Understand variables and performance implications of DAX formulas.
Power BI Fundamentals and Microsoft Ecosystem
Fundamentals of Power BI usage, including Power BI Desktop and Power BI Service, data modeling with DAX, report and dashboard design, data connectivity within the Microsoft ecosystem (Excel, SQL Server, Azure Synapse/Azure SQL Database, Azure Data Lake), and governance, security, deployment patterns, and best practices for BI solutions in Microsoft-centric environments
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.
Technical Skills and Tools
A concise but comprehensive presentation of a candidate's core technical competencies, tool familiarity, and practical proficiency. Topics to cover include programming languages and skill levels, frameworks and libraries, development tools and debuggers, relational and non relational databases, cloud platforms, containerization and orchestration, continuous integration and continuous deployment practices, business intelligence and analytics tools, data analysis libraries and machine learning toolkits, embedded systems and microcontroller experience, and any domain specific tooling. Candidates should communicate both breadth and depth: identify primary strengths, describe representative tasks they can perform independently, and call out areas of emerging competence. Provide brief concrete examples of projects or analyses where specific tools and technologies were applied and quantify outcomes or impact when possible, while avoiding long project storytelling. Prepare a two to three minute verbal summary that links skills and tools to concrete outcomes, and be ready for follow up probes about technical decisions, trade offs, and how tools were used to deliver results.