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.
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.
Python Data Manipulation with Pandas
Skills and concepts for extracting, transforming, and preparing tabular and array data in Python using libraries such as pandas and NumPy. Candidates should be comfortable reading data from common formats, working with pandas DataFrame and Series objects, selecting and filtering rows and columns, boolean indexing and query methods, groupby aggregations, sorting, merging and joining dataframes, reshaping data with pivot and melt, handling missing values, and converting and validating data types. Understand NumPy arrays and vectorized operations for efficient numeric computation, when to prefer vectorized approaches over Python loops, and how to write readable, reusable data processing functions. At higher levels, expect questions on memory efficiency, profiling and optimizing slow pandas operations, processing data that does not fit in memory, and designing robust pipelines that handle edge cases and mixed data types.
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.
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.
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 Skills & Tools Inventory
Be ready to discuss specific tools and platforms you're familiar with: marketing automation (HubSpot, Marketo, Klaviyo, ActiveCampaign), CRM systems (Salesforce, Pipedrive), analytics tools (Google Analytics, Mixpanel, Amplitude), data visualization (Tableau, Looker, Power BI), testing platforms (Optimizely, VWO), or data management platforms. For each tool, be specific about what you actually did (created reports, set up workflows, troubleshot issues, etc.), not just 'familiar with.' If you lack certain tools, mention your ability to learn technical systems quickly and provide examples of how you've picked up new platforms.
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.
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
Pandas Data Manipulation and Analysis
Data manipulation and analysis using the Pandas library: reading data from CSV or SQL sources, selecting and filtering rows and columns, boolean indexing, iloc and loc usage, groupby aggregations, merging and concatenating DataFrames, handling missing values with dropna and fillna, applying transformations via apply and vectorized operations, reshaping with pivot and melt, and performance considerations for large DataFrames. Includes converting SQL style logic into Pandas workflows for exploratory data analysis and feature engineering.