Hire Pre-vetted Data Analysts

What is a Data Analyst?

A Data Analyst is a specialist who transforms raw data into actionable insights that drive business decisions. Data Analysts do more than run queries—they conduct data exploration and discovery, design analyses to answer business questions, create visualizations that communicate findings clearly, and recommend actions based on data insights. Whether you need someone to build analytics infrastructure, analyze specific business problems, or help your organization become data-driven, a skilled Data Analyst brings analytical rigor and business acumen.

What makes Data Analysts valuable is their ability to bridge business and data. They understand business challenges deeply enough to ask the right questions, possess technical skills to extract answers from data, and communicate findings in ways decision-makers understand. This is why successful organizations invest in Data Analysts. When you hire through Torc, you're getting someone who helps your organization make better decisions.

Technology Stack

Data Querying & Analysis

• SQL (data extraction & analysis)

• Python (Pandas, NumPy for analysis)

• R for statistical analysis

• Excel/Google Sheets for exploration

Data Visualization

• Tableau, Power BI, Looker

• Google Data Studio

• Plotly, Matplotlib for Python

• Design principles for effective visualization

Databases & Data Warehouses

• SQL databases (PostgreSQL, MySQL)

• Data warehouse platforms (Snowflake, BigQuery)

• Data lakes & data lakehouses

• Data modeling & schema design

Statistical & Analytical Methods

• Descriptive & exploratory analysis

• Hypothesis testing

• A/B testing & experimentation

• Forecasting & trends

Business Intelligence

• BI tool development (dashboards, reports)

• KPI development & tracking

• Business metrics & definitions

• Reporting best practices

Key Qualities to Look For on a Data Analyst

Analytical Thinking — They approach problems systematically. They ask questions, explore data, form hypotheses, and validate conclusions rigorously.

Business Acumen — They understand business contexts, metrics, and decision-making. They ask the right questions and know what analyses matter for business success.

Technical Proficiency — They write efficient SQL, manipulate data programmatically, and use analysis tools proficiently. They solve technical problems independently.

Communication Skills — They translate technical findings into business language. They create visualizations that communicate clearly and tell compelling data stories.

Attention to Detail — They care about data quality. They validate data carefully, document assumptions, and ensure analyses are accurate.

Continuous Learning — They stay current with analytics tools and techniques. They experiment with new approaches and expand their analytical toolkit.

Project Types Your Data Analysts Handle

Analytics Implementation — Setting up analytics infrastructure and processes. Real scenarios: BI platform implementation, data warehouse setup, analytics process documentation.

Ad-Hoc Analysis — Answering specific business questions with data. Real scenarios: Customer behavior analysis, product performance analysis, market analysis.

Dashboard Development — Creating dashboards and reports for decision-making. Real scenarios: Executive dashboards, operational dashboards, department-specific reporting.

A/B Testing & Experimentation — Designing and analyzing experiments. Real scenarios: Product feature testing, marketing campaign testing, pricing experiments.

Data Quality & Governance — Ensuring data accuracy and consistency. Real scenarios: Data validation, quality assurance, metadata management.

Performance Analysis — Analyzing business performance and identifying opportunities. Real scenarios: Sales analysis, customer acquisition analysis, profitability analysis.

Forecasting & Predictive Analytics — Building models for prediction and forecasting. Real scenarios: Revenue forecasting, demand forecasting, churn prediction.

Interview questions

Question 1: "Walk me through an analysis you conducted. What question were you trying to answer, what data did you use, and what insights did you uncover?"

Why this matters: Tests analytical thinking and ability to turn data into insights. Reveals whether they ask the right questions, use appropriate methods, and communicate findings. Shows practical analytics experience.

Question 2: "Tell me about a time your analysis challenged assumptions or revealed something unexpected. How did you validate your findings and what happened with the insights?"

Why this matters: Tests critical thinking and ability to challenge conventional wisdom with data. Reveals whether they stop at obvious findings or dig deeper. Shows impact of their work.

Question 3: "Describe your experience with A/B testing or experimentation. What experiments have you run, how did you design them, and what did you learn?"

Why this matters: Tests experimental rigor and understanding of statistical concepts. Reveals whether they understand confounding variables, sample sizes, significance. Shows data-driven decision-making maturity.