Microsoft Data Analyst (Staff Level) - Comprehensive Interview Preparation Guide
Microsoft's Data Analyst interview process for Staff level is a rigorous, multi-stage evaluation spanning 4-6 weeks. It includes an initial recruiter screening, two technical phone screens covering SQL and advanced analytics, and four onsite rounds assessing technical mastery, business acumen, system design thinking, and leadership capabilities. The process emphasizes SQL proficiency, statistical analysis, business impact, and alignment with Microsoft's leadership principles (Create Clarity, Deliver Success). Staff-level candidates are expected to demonstrate deep expertise, the ability to mentor others, influence cross-functional teams, and contribute to data strategy.
Interview Rounds
Recruiter Screening
What to Expect
This initial conversation with Microsoft's recruiting team evaluates your background, motivation, and alignment with the Staff-level Data Analyst role. The recruiter will discuss your career trajectory, why you're interested in Microsoft, your understanding of the role and team, and your fit with Microsoft's culture and leadership principles. This is also your opportunity to ask questions about the role, team, and company. The recruiter will provide an overview of the interview process and answer preliminary questions. This round combines initial recruiter contact and any recruiter follow-up communication.
Tips & Advice
Be authentic and demonstrate genuine interest in Microsoft and the Data Analyst role. Prepare a concise summary of your career with emphasis on key accomplishments, business impact, and progression to Staff level. Research Microsoft's business model, products, recent initiatives, and data-driven decisions. Understand the team and department you're joining. Align your experience with Microsoft's leadership principles: Create Clarity (communicate clearly, make ambiguous situations clearer) and Deliver Success (own outcomes, drive results). For Staff level, emphasize mentorship, strategic thinking, and cross-functional influence. Prepare thoughtful questions about the team structure, how success is measured, opportunities to contribute to data strategy, and how Microsoft develops senior talent.
Focus Topics
Alignment with Microsoft Leadership Principles
Discuss how your approach to work aligns with Microsoft's core principles: Create Clarity (making ambiguous situations clearer, communicating effectively to stakeholders, clarifying data insights) and Deliver Success (owning outcomes, driving measurable results, following through). Provide specific examples from your career demonstrating these principles.
Practice Interview
Study Questions
Understanding Microsoft's Business Model and Data Strategy
Show familiarity with Microsoft's products (Azure cloud services, Power BI, Office 365, Windows, Teams, LinkedIn, GitHub) and revenue streams. Demonstrate understanding of how data analytics drives value across Microsoft's diverse portfolio. Mention specific data-driven insights or strategies you believe Microsoft could leverage.
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Study Questions
Motivation for Microsoft and the Role
Clearly articulate why you want to join Microsoft specifically and why the Data Analyst role aligns with your career goals. Connect your analytical expertise with Microsoft's mission of empowering every person and organization to achieve more. Reference specific Microsoft products or initiatives you're excited about.
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Study Questions
Career Trajectory and Staff-Level Progression
Articulate your professional journey from early career analyst to Staff level over 12+ years, highlighting key milestones, growth in analytical complexity, expanding scope of responsibility, and increasing business impact. Explain what Staff level means to you—deep technical expertise, mentorship of junior colleagues, strategic contribution to data initiatives—and how your experience authentically aligns with this definition.
Practice Interview
Study Questions
Technical Phone Screen 1 - SQL & Data Manipulation
What to Expect
This 45-60 minute technical phone screen focuses on SQL proficiency and data manipulation skills, which are foundational for the Data Analyst role at any level but especially for Staff. You will be asked to solve real-world SQL problems on a shared coding platform or whiteboard tool. Expect questions involving complex database queries, multiple joins, aggregations, window functions, and performance optimization. The interviewer will assess your ability to write clear, efficient queries and explain your problem-solving approach. You'll be evaluated on correctness, code quality, optimization thinking, and handling edge cases.
Tips & Advice
Start by clarifying requirements and asking about data structure, constraints, and expected output. Begin with a straightforward solution to show problem comprehension, then discuss optimization strategies (indexes, query refactoring, partitioning, caching). For Staff level, interviewers expect not just correct solutions but also sophisticated thinking about scalability, data quality edge cases, and performance on production datasets with billions of rows. Communicate your thought process step-by-step; explain why you're making certain decisions. Practice on platforms like LeetCode or HackerRank focusing on database problems. Be prepared to discuss how your query would perform with 1B+ rows and how you'd optimize further. Address potential data quality issues in your solution (nulls, duplicates, data type mismatches). Demonstrate awareness of different SQL dialects if relevant.
Focus Topics
Data Integrity and Quality Handling
Proactively address common data quality issues in SQL: null values (understanding NULL logic), duplicates (detecting and handling), data type mismatches, inconsistencies, and invalid values. Write queries that validate data assumptions and handle edge cases gracefully.
Practice Interview
Study Questions
Window Functions and Advanced SQL Techniques
Utilize window functions effectively: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, and aggregation functions with OVER clause to solve complex analytical problems. Know when to use window functions vs. traditional aggregations and GROUP BY. Use window functions for time-series analysis, running totals, and rankings.
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Study Questions
Real-World SQL Scenarios and Business Context
Solve SQL problems in realistic business contexts: user behavior analysis, revenue attribution, sales funnel analysis, cohort retention analysis, A/B testing metric calculations, and product usage analytics. Understand business metrics and how to calculate them correctly.
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Query Optimization and Performance Tuning
Explain strategies to optimize query performance for large-scale datasets: using indexes effectively, understanding query execution plans, data partitioning, avoiding N+1 query problems, and refactoring complex queries for better performance. Discuss trade-offs between readability and performance. Address how optimization changes with data volume (1K rows vs. 1B rows).
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Study Questions
Complex SQL Query Writing
Write efficient SQL queries involving multiple joins (inner, left, right, full outer), complex subqueries, Common Table Expressions (CTEs), and sophisticated filtering logic. Demonstrate understanding of different join types and when to use them. Handle complex business logic and calculations within queries. Show ability to break down complex problems into manageable SQL statements.
Practice Interview
Study Questions
Technical Phone Screen 2 - Statistical Analysis & Advanced Analytics
What to Expect
This 45-60 minute phone screen assesses your statistical knowledge, analytical thinking, and ability to derive insights from data. You may be asked to discuss statistical concepts, design experiments, interpret data, solve analytical case studies, or discuss causal inference. The interviewer will evaluate your understanding of hypothesis testing, probability distributions, A/B testing, and your ability to translate statistical findings into business recommendations. For Staff level, expect deep questions about assumptions, when methods break down, and advanced analytical techniques.
Tips & Advice
Review core statistical concepts: probability distributions (normal, binomial, Poisson), hypothesis testing, p-values, confidence intervals, statistical power, and Type I/II errors. Understand when and why to use different statistical tests and their underlying assumptions. Be prepared to discuss A/B testing design comprehensively: hypothesis formulation, sample size calculations (power analysis), statistical power, potential confounds, sequential testing, and multiple comparison corrections. For Staff level, expect deep questions about violations of statistical assumptions, when to use non-parametric methods, and alternative approaches when classical methods aren't appropriate. Practice explaining statistical concepts clearly to non-statisticians. Prepare specific examples from your experience where statistical analysis drove business decisions. Be ready to critique poorly-designed experiments and suggest improvements. Discuss concepts like Simpson's Paradox, causal inference, and handling selection bias.
Focus Topics
Data-Driven Decision Making and Business Impact
Translate statistical findings and analytical insights into actionable business recommendations. Quantify business impact and ROI of recommendations. Discuss methodology for causal inference, handling selection bias, and establishing causation vs. correlation. Address risk assessment and uncertainty quantification.
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Study Questions
Predictive Modeling and Statistical Analysis
Explain predictive modeling techniques: regression analysis (linear, logistic, multiple regression), model validation (train/test splits, cross-validation), overfitting vs. underfitting, feature selection and engineering, and model performance metrics. Discuss appropriate evaluation strategies for different scenarios (classification, regression, time-series).
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Study Questions
User Behavior Analysis and Segmentation
Apply analytical techniques to understand user behavior: cohort analysis (tracking user cohorts over time), retention and churn analysis, funnel analysis (identifying drop-off points), and customer segmentation. Identify trends, patterns, and anomalies in user activity. Discuss how to measure user engagement and satisfaction.
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Study Questions
Statistical Concepts and Hypothesis Testing
Demonstrate mastery of fundamental statistical concepts: probability distributions (normal, binomial, Poisson), hypothesis testing framework, p-values and their limitations, confidence intervals, statistical power, Type I/II errors, and significance testing. Explain when each concept applies and how to interpret results correctly. Discuss violations of statistical assumptions and how to detect them.
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Study Questions
A/B Testing and Experimental Design
Design and analyze A/B tests comprehensively: formulate testable hypotheses, calculate required sample sizes using power analysis, discuss statistical power and sensitivity, identify potential confounds and biases, implement proper randomization, analyze results correctly, and handle multiple comparison corrections. Understand sequential testing and early stopping rules. Address common A/B testing pitfalls.
Practice Interview
Study Questions
Onsite Interview Round 1 - SQL & Coding Challenge
What to Expect
In this 45-60 minute onsite round, you will solve a live SQL coding challenge using a computer and shared screen with the interviewer. This round assesses your ability to write correct, efficient SQL under pressure with an audience observing your thought process. The interviewer will ask clarifying questions, observe your problem-solving methodology, and may ask you to optimize your solution or handle additional requirements. You'll be evaluated on correctness, code quality, optimization thinking, communication, and adaptability when requirements evolve.
Tips & Advice
Approach this systematically: clarify requirements by asking questions, discuss your overall approach before coding, start with a working solution to show problem comprehension, then optimize and discuss scalability. Write clean, readable SQL with meaningful aliases and comments. For Staff level, demonstrate sophisticated thinking about data scale—discuss how your solution would perform with 1 billion rows, where bottlenecks might be, and what optimizations you'd apply. Think proactively about edge cases: nulls, duplicates, data type issues, empty result sets, and boundary conditions. Ask for feedback and be willing to iterate based on interviewer suggestions. Communicate throughout; explain your reasoning and thought process clearly. If stuck, discuss alternative approaches rather than staying silent. Practice under timed conditions to build confidence and speed. Show self-correction—if you realize an issue with your approach, acknowledge it and fix it.
Focus Topics
Handling Ambiguity and Adapting to Requirements
Ask clarifying questions when requirements are ambiguous or incomplete. Make reasonable assumptions and communicate them to the interviewer. Adapt when requirements change or interviewer suggests different approaches. Show flexibility and willingness to explore alternatives.
Practice Interview
Study Questions
Query Optimization for Large-Scale Data
Optimize queries for performance on production-scale datasets: understand query execution plans, appropriate use of indexes (when to add, when they hurt performance), join optimization strategies, avoiding expensive operations (full table scans, Cartesian products), and discussing scalability considerations as data grows.
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Code Quality and Communication
Write clean, readable SQL with meaningful variable names, logical structure, and comments where appropriate. Explain your thought process throughout the coding session. Structure queries for maintainability and future understanding. Show professionalism in code organization.
Practice Interview
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Query Correctness and Edge Cases
Write SQL that handles edge cases correctly: null values (understanding three-valued logic), duplicates (detecting and removing), data type mismatches, empty result sets, boundary conditions, and invalid data. Verify assumptions about data and communicate them to the interviewer.
Practice Interview
Study Questions
SQL Problem-Solving Methodology
Systematically approach SQL problems: understand requirements through clarifying questions, design solution architecture before coding, implement step-by-step, test assumptions, verify correctness with edge cases, and optimize for performance. Communicate your approach before writing code.
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Study Questions
Onsite Interview Round 2 - Business Case Study & Analysis
What to Expect
In this 45-60 minute round, you will work through a realistic business case study or analytical problem that mirrors actual work at Microsoft. You might be asked to analyze a business scenario, recommend data-driven decisions, propose solutions to analytical challenges, or evaluate options based on data. The interviewer will provide business context, data descriptions, and specific questions. You'll be evaluated on analytical thinking, business acumen, ability to translate data into insights, quantification of impact, and clear communication of findings to stakeholders.
Tips & Advice
For Staff level, this round assesses strategic thinking and business impact—not just technical skill. Start by understanding the business context deeply and the specific question being asked. Clarify what data is available and what you'd need to collect. Structure your approach: define appropriate metrics, identify key drivers and relationships, propose sound analytical methodology. Make reasonable assumptions and communicate them. Calculate or estimate business impact where possible. For Staff-level roles, demonstrate awareness of complexity: multiple stakeholders, trade-offs, implementation considerations, and risks. Discuss how you would prioritize among multiple analytical questions. Show how you would communicate findings to executives in language they understand. Discuss potential unintended consequences and how to mitigate risks. Practice thinking like a business leader, not just a technician. Address both 'what' (findings) and 'so what' (implications for business).
Focus Topics
Stakeholder Communication and Influence
Communicate findings clearly to non-technical stakeholders at all levels. Adjust communication style and depth for different audiences (executives vs. individual contributors). Make compelling cases for recommendations. Anticipate stakeholder concerns and address them proactively. Show how analysis aligns with business priorities.
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Study Questions
Analytical Methodology and Approach
Propose sound analytical approaches to business questions. Identify appropriate metrics and measurement approaches. Distinguish between correlation and causation. Suggest experiments or analyses that would validate hypotheses. Discuss limitations of your approach and alternative methods.
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Business Impact Quantification and ROI
Estimate business impact of proposed solutions quantitatively. Calculate return on investment, discuss cost-benefit trade-offs, and identify risks or potential unintended consequences. Frame impact in terms that matter to the business (revenue, cost savings, efficiency, customer satisfaction).
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Data-Driven Insights and Recommendations
Translate data findings into clear, actionable business recommendations. Connect analytical insights directly to business outcomes and strategy. Quantify impact, discuss trade-offs, and address implementation considerations. Explain your reasoning in terms business stakeholders understand, not just technical language.
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Business Problem Analysis and Scoping
Quickly understand business context and problem statements. Break down ambiguous problems into smaller analytical questions. Identify key metrics and success criteria that matter to the business. Scope analysis appropriately—determine what's in scope and what's out of scope to maximize impact. Clarify stakeholder expectations and success metrics.
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Onsite Interview Round 3 - Data Architecture, Tools & Visualization Design
What to Expect
This 45-60 minute round focuses on your expertise with data tools, dashboarding, data architecture, and analytical infrastructure. You may be asked to design a dashboard for a specific business question, discuss data architecture choices, recommend tools for analytical use cases, evaluate existing dashboard or data pipeline approaches, or discuss scalability and performance considerations. The interviewer assesses your knowledge of Power BI, data modeling, visualization best practices, and understanding of data pipelines and ETL processes.
Tips & Advice
Prepare to discuss dashboard design principles comprehensively: clarity, information hierarchy, appropriate visualizations for different data types, color usage, interactivity, and user experience. Know Power BI deeply: data models and relationships, DAX calculations, visualization options and when to use each, filtering and slicing mechanisms, performance optimization (aggregations, storage modes), and publishing/sharing approaches. For Staff level, discuss scalability: how dashboards should be designed to handle growing data volumes without performance degradation. Understand ETL concepts and data pipeline architecture—bottlenecks, latency, reliability. Discuss data modeling best practices: dimensional modeling, star schemas, slowly changing dimensions. Be ready to evaluate existing dashboards and suggest improvements. Think about data governance, documentation, and collaboration between analytics and engineering teams. Discuss the balance between self-service analytics and centralized data governance.
Focus Topics
Data Tools Selection and Azure Technology Stack
Recommend appropriate tools for different analytical use cases. Understand Microsoft's technology ecosystem: Azure services (Azure SQL, Azure Synapse, Data Lake), SQL Server, Power BI, Excel, Python, and when to use each. Consider scalability, cost, maintainability, and team expertise when recommending tools.
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Study Questions
Data Modeling and Star Schema Design
Design effective data models that enable fast, accurate analysis: understand fact and dimension tables, their relationships, slowly changing dimensions (SCD types), aggregation tables, and denormalization strategies. Discuss normalization vs. denormalization trade-offs. Create data models that support self-service analytics.
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Study Questions
ETL Processes and Data Pipeline Understanding
Understand extract, transform, load processes comprehensively: data source connections, transformation logic and validation, data quality checks, error handling, scheduling and orchestration, monitoring and alerting, and troubleshooting common pipeline issues. Evaluate tools like Azure Data Factory, SQL Server Integration Services (SSIS), or alternatives.
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Study Questions
Power BI Expertise and Implementation
Demonstrate deep knowledge of Power BI: creating and optimizing data models, understanding relationships and cardinality, writing complex DAX calculations, selecting appropriate visualization types, implementing filtering and slicing, optimizing performance, publishing and sharing reports, row-level security (RLS), and refresh strategies.
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Dashboard Design and Visualization Best Practices
Design effective dashboards that communicate data clearly to different audiences. Apply principles like visual hierarchy, appropriate chart types (when to use bar vs. line vs. scatter), color usage psychology, white space, and interactivity. Balance aesthetic appeal with functional clarity. Consider how dashboards are consumed and used in practice.
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Study Questions
Onsite Interview Round 4 - Leadership, Mentorship & Strategic Impact
What to Expect
This 45-60 minute behavioral and leadership round evaluates how you lead, influence, mentor, and contribute to strategy at the Staff level. The interviewer will discuss your experience mentoring junior and mid-level analysts, influencing cross-functional teams without formal authority, handling ambiguous situations, and contributing to data strategy and organizational initiatives. You'll be asked about your career philosophy, how you develop team members, examples of significant business impact, your vision for data analytics, and how you embody Microsoft's leadership principles.
Tips & Advice
For Staff level, this round is crucial—it assesses whether you truly operate at Staff level as a senior individual contributor or tech lead. Prepare specific, detailed examples of: (1) mentoring junior analysts—how you helped them grow, specific techniques you used, measurable improvements; (2) influencing without authority—how you got buy-in for an approach you believed in, especially when facing skepticism; (3) navigating ambiguity—handling unclear requirements, conflicting priorities, incomplete data, or changing business context; (4) measurable business impact—quantified outcomes from your work (revenue influenced, decisions improved, efficiency gains). Use the STAR method (Situation, Task, Action, Result) but emphasize the impact and lessons learned. Discuss your approach to continuous learning and staying current with data tools and techniques. Be authentic about leadership philosophy—Staff-level analysts lead through expertise and credibility, not hierarchy. Explain how you contribute beyond your specific project to the broader data analytics function. Prepare thoughtful questions about how Microsoft develops and retains senior talent and contributes to strategic data initiatives.
Focus Topics
Strategic Thinking about Data and Analytics
Discuss your vision for analytics' role in business and organizational strategy. Share ideas about scaling analytics capabilities, building data-driven culture, identifying high-impact analytical problems, investing in analytics tools and talent. Discuss how you've contributed to analytics strategy.
Practice Interview
Study Questions
Alignment with Microsoft Leadership Principles: Create Clarity & Deliver Success
Demonstrate authentic alignment with Microsoft's core principles through specific examples. Create Clarity: making ambiguous situations clear, improving team communication about data insights, helping stakeholders understand complex analysis. Deliver Success: owning outcomes, driving results, following through on commitments, taking responsibility for delivery.
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Measurable Business Impact and Outcomes
Quantify your impact comprehensively: revenue influenced or protected, decisions improved, efficiency gains, cost savings, or customer satisfaction improvements driven by your analytics work. Discuss how you measure success beyond technical metrics. Explain how you ensure analytics drives tangible business results.
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Study Questions
Navigating Ambiguity and Complex Problems
Provide examples of handling ambiguous situations: unclear requirements, conflicting priorities from different stakeholders, incomplete or poor quality data, rapidly changing business context. Show how you clarified the problem, made reasonable assumptions, communicated them, and delivered value despite uncertainty.
Practice Interview
Study Questions
Influencing Cross-Functional Teams and Stakeholders
Describe situations where you influenced decisions, drove adoption of new analytics approaches, or gained buy-in for data-driven initiatives without formal authority. Discuss how you built credibility, handled resistance, navigated politics, and eventually succeeded. Explain your influence strategy.
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Study Questions
Mentorship and Developing Other Analysts
Share detailed examples of mentoring junior and mid-level data analysts. Discuss your approach to coaching: how you identify skill gaps, provide feedback, create development plans, and support growth. Share specific examples of analysts you've developed into stronger contributors. Discuss how you measure mentoring effectiveness.
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Frequently Asked Data Analyst Interview Questions
Sample Answer
-- 1) Close out changed current rows
MERGE INTO product_dim d
USING (
SELECT s.product_id, s.name, s.price
FROM stg_products s
) s
ON d.product_id = s.product_id AND d.current_flag = 1
WHEN MATCHED AND (d.name <> s.name OR d.price <> s.price OR (d.name IS NULL AND s.name IS NOT NULL) OR (d.price IS NULL AND s.price IS NOT NULL))
THEN UPDATE SET
effective_to = CURRENT_TIMESTAMP(),
current_flag = 0;
-- 2) Insert new rows (new product_ids or new versions)
INSERT INTO product_dim (product_key, product_id, name, price, effective_from, effective_to, current_flag)
SELECT
NEXTVAL('product_dim_seq') AS product_key, -- or use SEQUENCE_NEXTVAL()
s.product_id,
s.name,
s.price,
CURRENT_TIMESTAMP() AS effective_from,
'9999-12-31'::TIMESTAMP AS effective_to,
1 AS current_flag
FROM stg_products s
LEFT JOIN product_dim d
ON s.product_id = d.product_id AND d.current_flag = 1
WHERE d.product_id IS NULL -- brand new product
OR d.name <> s.name
OR d.price <> s.price
OR (d.name IS NULL AND s.name IS NOT NULL)
OR (d.price IS NULL AND s.price IS NOT NULL);Sample Answer
Sample Answer
Sample Answer
Sample Answer
SELECT order_id, user_id, amount, order_date
FROM (
SELECT
order_id,
user_id,
amount,
order_date,
ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY amount DESC, order_date DESC) AS rn
FROM orders
) t
WHERE rn <= 3;SELECT u.user_id, o.order_id, o.amount, o.order_date
FROM (SELECT DISTINCT user_id FROM orders) u
CROSS JOIN LATERAL (
SELECT order_id, amount, order_date
FROM orders o
WHERE o.user_id = u.user_id
ORDER BY amount DESC, order_date DESC
LIMIT 3
) o;Sample Answer
SELECT
id,
amount_text,
-- convert "(1,234.56)" -> "-1234.56", remove $ and commas/spaces
regexp_replace(
translate(
regexp_replace(amount_text, '^\s*\((.*)\)\s*$', '\1'), -- strip outer parentheses content
'$, ', '') -- remove $, comma, space
, '^([0-9]+(\.[0-9]+)?)$', '\1') -- placeholder for clarity
AS cleaned_candidate,
CASE
WHEN regexp_replace(amount_text, '^\s*\((.*)\)\s*$', '-\1') ~ '^[+-]?[0-9]+(\.[0-9]+)?$'
THEN (regexp_replace(amount_text, '^\s*\((.*)\)\s*$', '-\1')::numeric)
ELSE NULL
END AS amount_cast
FROM my_table;SELECT
id,
amount_text,
-- 1) move parentheses to leading '-' 2) remove $, commas, spaces 3) collapse multiple +/-
cleaned,
CASE WHEN cleaned ~ '^[+-]?[0-9]+(\.[0-9]+)?$' THEN cleaned::numeric ELSE NULL END AS amount
FROM (
SELECT
id,
amount_text,
trim(
translate(
regexp_replace(amount_text, '^\s*\((.*)\)\s*$', '-\1'), -- (x) -> -x
'$, ', '') -- remove $, commas, spaces
) AS cleaned
FROM my_table
) t;-- add column
ALTER TABLE my_table ADD COLUMN amount numeric;
-- populate only valid rows
UPDATE my_table
SET amount = cleaned::numeric
FROM (
SELECT id, trim(translate(regexp_replace(amount_text, '^\s*\((.*)\)\s*$', '-\1'), '$, ', '')) AS cleaned
FROM my_table
) c
WHERE my_table.id = c.id
AND c.cleaned ~ '^[+-]?[0-9]+(\.[0-9]+)?$';
-- surface rows that failed (need manual review)
SELECT id, amount_text, trimmed AS cleaned
FROM (
SELECT id, amount_text,
trim(translate(regexp_replace(amount_text, '^\s*\((.*)\)\s*$', '-\1'), '$, ', '')) AS trimmed
FROM my_table
) x
WHERE NOT (trimmed ~ '^[+-]?[0-9]+(\.[0-9]+)?$') OR trimmed = '';Sample Answer
Sample Answer
Sample Answer
Sample Answer
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