Key Responsibilities and Required Skills for Analyst

🎯 Role Definition

An Analyst plays a central role in turning raw data into actionable insights that drive business decisions. This role is responsible for data collection, cleansing, analysis, reporting, and presenting results to stakeholders across functions. Analysts bridge the gap between technical teams and business leaders by translating requirements into repeatable analyses, building dashboards, and recommending measurable actions to improve performance, efficiency, and revenue. Typical focus areas include business intelligence, financial analysis, product analytics, marketing analytics, and operations reporting.

📈 Career Progression

Typical Career Path

Entry Point From:

Junior Data Analyst / Reporting Specialist
Business Operations Coordinator with strong Excel/SQL experience
Internship in analytics, finance, or market research

Advancement To:

Senior Analyst / Lead Analyst
Business Intelligence (BI) Developer or Analytics Manager
Product Analytics Manager / Data Scientist (with further upskilling)

Lateral Moves:

Data Engineer (with ETL and engineering focus)
Business Operations Manager
Financial Planning & Analysis (FP&A) Specialist

Core Responsibilities

Primary Functions

Design, develop, and maintain complex SQL queries, stored procedures and data pipelines to extract, transform, and load (ETL) data from multiple transactional and analytical sources to support reporting and ad-hoc analysis.
Build, maintain and iterate on interactive dashboards and visualizations in Tableau, Power BI, Looker, or similar tools to provide executives and functional teams with timely insights and KPI tracking.
Collect, clean, validate and reconcile large datasets using Python, R, Excel, or internal tools to ensure data accuracy and integrity for downstream analysis and decision-making.
Translate ambiguous business problems into structured analytical plans, define success metrics, and deliver reproducible analyses and recommendations that align with strategic goals.
Perform cohort analysis, segmentation, funnel analysis, and A/B test interpretation to evaluate product, marketing, and user engagement initiatives and quantify impact on revenue, retention and growth.
Develop and maintain standardized reporting packages and recurring monthly, weekly and daily operational reports that document trends, anomalies and business performance drivers.
Partner directly with product managers, marketing, sales, finance and operations to gather requirements, prioritize analytics projects, and deliver data-backed recommendations that influence roadmap and budgeting decisions.
Create and document data dictionaries, analytics methodologies and runbooks to ensure consistent interpretation of metrics and enable cross-functional self-service.
Implement data quality checks, monitoring and alerting processes to detect and resolve discrepancies, missing values or pipeline failures before they affect stakeholders.
Use statistical modeling and forecasting techniques (time series, regression, propensity score matching) to produce demand forecasts, churn predictions and scenario planning for leadership.
Conduct root cause analyses on business KPIs and operational incidents, produce clear findings and recommended mitigations, and follow through with implementation support.
Automate manual reporting and repetitive analytics tasks using Python scripts, scheduled database jobs, or BI tool capabilities to increase team efficiency and reduce error rates.
Perform cost-benefit and ROI analysis for strategic initiatives, pricing experiments and marketing campaigns to recommend resource allocation and prioritize investments.
Design and analyze experiments (A/B tests), write clear test plans, calculate sample sizes, monitor experiments, and produce interpretation and next-step recommendations to product and marketing owners.
Aggregate and synthesize cross-functional data (CRM, finance, marketing platforms, web analytics, product telemetry) to create unified views of customer behavior and business performance.
Present analytical results and strategic recommendations to senior leadership and cross-functional teams in clear, concise slide decks and presentations tailored to the audience.
Partner with data engineering to translate business needs into data models, schema changes, and incremental pipeline improvements that enable faster, more accurate analytics.
Maintain and monitor key performance indicators (KPIs) and SLAs, advising stakeholders when strategic adjustments are required and tracking the impact of implemented changes.
Ensure compliance with data privacy, governance, and security standards when accessing, storing and sharing sensitive or personally identifiable information (PII).
Mentor junior analysts, conduct code and dashboard reviews, and help establish best practices for analysis, visualization, and documentation across the analytics team.
Lead cross-functional projects to implement new analytics solutions or integrate third-party data sources, coordinating timelines, requirements and stakeholder expectations.
Continuously research and evaluate new analytics tools, BI technologies and industry techniques to recommend improvements to the analytics stack and increase organizational capability.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Assist in vendor evaluation and onboarding for analytics, BI, and data enrichment services.
Maintain version control for analytical scripts and dashboards and follow CI/CD or deployment guidelines for analytics artifacts.
Provide training and analytics enablement sessions to business users to increase self-service reporting adoption.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL: complex joins, window functions, CTEs, query optimization and query profiling for large datasets.
Data visualization tools: Tableau, Power BI, Looker, or equivalent experience designing dashboards and visual analytics.
Programming for analysis: Python (pandas, numpy, matplotlib/seaborn) and/or R for cleaning, analysis and automation.
Spreadsheet mastery: Advanced Excel skills including pivot tables, INDEX/MATCH, advanced formulas and VBA or macros where applicable.
ETL and data pipelines: familiarity with tools and processes for data ingestion, transformation and orchestration (Airflow, dbt, Talend, Informatica).
Data modeling and dimensional modeling: building star schemas, fact and dimension tables to support performant analytics.
Statistical analysis and experimentation: hypothesis testing, A/B testing frameworks, regressions, confidence intervals and power calculations.
Web and product analytics: experience with Google Analytics, Mixpanel, Amplitude or similar to instrument and analyze user behavior.
Cloud and database platforms: experience with relational databases (Postgres, MySQL), data warehouses (Snowflake, Redshift, BigQuery) and/or cloud platforms (AWS, GCP, Azure).
Reporting automation and scripting: cron scheduling, API integrations, and use of tools to automate data refreshes and report delivery.
Familiarity with data governance, PII handling, GDPR/CCPA considerations, and role-based access control in analytics tools.
Basic familiarity with machine learning concepts and libraries (scikit-learn, XGBoost) for building predictive models where applicable.
Experience with business systems (CRM like Salesforce, ERP, marketing automation platforms) and ability to join data across systems.

Soft Skills

Strong communication and storytelling: explain complex analyses and translate technical findings into business recommendations for non-technical stakeholders.
Stakeholder management: prioritize requests, negotiate trade-offs, and maintain clear expectations with product, finance, marketing and operations teams.
Critical thinking and problem solving: break down ambiguous problems into testable hypotheses and iterative solutions.
Attention to detail and data quality orientation: meticulous validation and reconciliation to maintain trust in analytics outputs.
Time management and prioritization: manage multiple competing requests and projects while meeting deadlines.
Collaboration and teamwork: work cross-functionally with engineering, product and business teams to deliver outcomes.
Influencing without authority: drive adoption of insights and recommendations through persuasion and evidence-based arguments.
Adaptability and continuous learning: quickly adopt new tools, datasets, or methodologies as business needs evolve.
Presentation and visualization design: craft clear, actionable dashboards and slide decks that facilitate decision-making.
Ethical judgment and confidentiality: handle sensitive financial or customer data responsibly and adhere to compliance standards.

Education & Experience

Educational Background

Minimum Education:

Bachelor’s degree in a quantitative, technical, or business discipline (e.g., Economics, Statistics, Mathematics, Computer Science, Engineering, Finance, Business Analytics).

Preferred Education:

Master’s degree or postgraduate certificate in Data Analytics, Business Analytics, Statistics, Applied Mathematics, Data Science, or MBA with analytics emphasis.

Relevant Fields of Study:

Data Science / Analytics
Statistics / Applied Mathematics
Computer Science / Software Engineering
Economics / Finance
Business Administration / Management Information Systems

Experience Requirements

Typical Experience Range:

1–5 years of hands-on experience in analytics, business intelligence, data reporting, or related roles for mid-level Analyst positions. Entry-level may be 0–2 years; senior roles typically 4–8+ years.

Preferred:

3+ years of experience building dashboards, writing complex SQL, conducting statistical analyses and presenting insights to cross-functional stakeholders. Prior experience in the relevant industry (e.g., SaaS, e-commerce, finance, health tech) is often highly valued.