Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Engineering and Business Outcomes
How engineering work and technical decisions translate into measurable business outcomes and how to demonstrate that linkage. Topics include mapping architecture choices, reliability, performance improvements and developer productivity initiatives to business metrics such as revenue, customer engagement, time to market, cost reduction and customer satisfaction. Candidates should be able to identify engineering metrics to track including latency, availability, error and incident rates, cycle time and deployment frequency, explain instrumentation strategies to capture signals, design measurement plans and experiments to establish causal impact, and attribute observed changes to specific engineering efforts. This topic also covers communicating technical tradeoffs and impact to nontechnical stakeholders, choosing appropriate granularity for measurement, and describing concrete initiatives with their measurement approach and quantified business impact.
Structured Query Language Fundamentals and Aggregation
This topic covers core Structured Query Language fundamentals for analytical querying and reporting. Candidates should be able to write correct, readable, and maintainable SELECT queries with filtering using WHERE, sorting with ORDER BY, grouping with GROUP BY, and group filtering with HAVING. They should apply aggregate functions such as COUNT, COUNT DISTINCT, SUM, AVG, MIN, and MAX and understand how NULL values affect results, how empty result sets behave, and when to use different counting approaches. The scope includes date and time filtering, basic cohort segmentation, and common time based comparisons used to compute metrics such as daily active users, average revenue per user, and period over period comparisons. Candidates are expected to use basic joins and join predicates including inner joins and left joins, write simple subqueries and conditional expressions, and perform common data transformation and cleansing patterns to prepare data for analysis. Finally, this topic assesses query readability and maintainability practices such as aliasing and formatting, plus awareness of elementary performance considerations including index usage and avoiding unnecessary full table scans for entry to mid level analytical tasks.
Analytical Background
The candidate's approach to analytical, evidence-based problem solving: how they take an ambiguous question, break it into testable pieces, gather and examine relevant information or data, choose appropriate methods to reach a conclusion, and turn that conclusion into a concrete recommendation or decision. This can show up as quantitative work (statistics, data analysis, experimentation, dashboards) or as qualitative and domain-specific analysis (reviewing logs or incidents, case or contract research, market or process analysis, root-cause investigation). Draw on academic projects, internships, or professional work. Focus on the end-to-end path: how the question or hypothesis was framed, what evidence was examined and with what tools or methods, what trade-offs were considered, and how the resulting insight changed a real decision or outcome.
SQL for Business Scenarios
Ability to read and decompose business questions and translate them into correct, efficient Structured Query Language queries that answer those questions. This includes identifying the required data sources and joins, choosing between inner joins, outer joins, anti joins and existence checks, writing subqueries and common table expressions for clarity, and applying filtering with where clauses, aggregation with group by and having, and window functions for ranking, running totals, and time series calculations. Candidates should demonstrate how to implement common business analyses such as conversion funnels, retention and cohort analysis, churn and lifetime value calculations, and operational metrics by mapping metric definitions to SQL expressions and handling edge cases like null values, duplicates, and late arriving data. The description also covers writing medium complexity queries that combine multiple tables, calculating derived metrics, validating results with sample data, and considering query performance through basic optimization techniques, indexing awareness, and selective projection.
Data and Trend Analysis with Pattern Recognition
Analyzing quantitative and qualitative data to identify patterns, trends, correlations, and meaningful insights. Skills assessed include descriptive statistics, time series and trend analysis, visualization and dashboarding, hypothesis generation and testing, identifying seasonality and structural changes, distinguishing signal from noise, and synthesizing findings into clear recommendations. For qualitative inputs candidates should demonstrate coding, theme extraction, categorization, and synthesis of transcripts or survey responses. Emphasis is on choosing appropriate methods, validating patterns, avoiding common pitfalls such as confounding and spurious correlation, and communicating insights effectively to stakeholders.
Data Investigation and Root Cause Analysis
Techniques and a structured process for diagnosing an unexpected change in a metric, dataset, or system signal using quantitative evidence complemented by qualitative signals. Candidates should demonstrate how to validate that an observed change is a real signal and not noise, or a reporting, instrumentation, or pipeline problem, by checking data quality, event or record counts, sampling, schema stability, and pipeline or data-flow integrity. Describe slicing and decomposition strategies such as cohort or population segmentation, geography and platform segmentation, feature-level analysis, time series decomposition to separate trend and seasonality, funnel and velocity analysis, retention analysis, and variance analysis. Explain how to form, prioritize, and test hypotheses; design diagnostic queries and tests using structured query language or equivalent tooling; and correlate the change with plausible triggers such as releases or deployments, configuration or schema changes, experiments, campaigns, upstream system incidents, or external events. Include how to combine quantitative findings with qualitative evidence such as interviews, logs, session or trace replay, support tickets, or incident timelines to strengthen causal inference. Finally, cover communicating concise findings and actionable recommendations to stakeholders, creating reproducible queries and monitoring dashboards, alerts, or runbooks, and mentoring others on a systematic investigation approach. This applies broadly to investigating anomalies in business metrics, product data, system or service health signals, financial figures, or model performance, not only one of these domains.