Key Responsibilities and Required Skills for Data Analyst

🎯 Role Definition

As a Data Analyst, you will serve as the bridge between raw data and business decision-making, transforming complex datasets into meaningful insights. You will design and maintain dashboards, generate reports, perform statistical analysis, and collaborate with stakeholders across marketing, finance, operations, and product teams. The role requires proficiency in SQL, Excel, and data visualization tools, strong analytical thinking, attention to detail, and the ability to communicate findings clearly to non-technical audiences. You will also contribute to the organization’s data strategy, mentor junior analysts, and stay up-to-date with emerging analytics trends and technologies.

📈 Career Progression

Typical Career Path

Entry Point From:

Junior Data Analyst or Business Intelligence Analyst
Reporting Analyst or Data Operations Specialist
Analytics Intern or Associate Data Scientist

Advancement To:

Senior Data Analyst
Data Analytics Lead or BI Manager
Data Scientist or Analytics Architect

Lateral Moves:

Business Intelligence Developer
Analytics Consultant
Data Product Manager

Core Responsibilities

Primary Functions

Lead end‑to‑end data collection, extraction and transformation efforts: gather data from various internal and external sources, implement cleaning and validation processes to ensure data accuracy and integrity.
Design, build and maintain dashboards, reports and visualizations that translate complex analytical results into clear business insights for non‑technical stakeholders.
Conduct exploratory data analysis (EDA) to identify patterns, trends, correlations, outliers and anomalies within datasets and derive actionable recommendations.
Collaborate with business units, product teams and senior leadership to define analytics requirements, determine key performance indicators (KPIs) and align data initiatives with strategic objectives.
Create and execute statistical models, segmentation, forecasting or predictive analyses when required to support data‑driven decision making.
Manage and optimise data systems and databases: design data architecture, maintain relational and/or NoSQL systems, monitor performance and ensure efficient data access.
Ensure data governance, compliance and privacy standards are upheld: monitor data quality and validity, implement checks, correct inconsistencies and maintain audit trails.
Develop efficient data pipelines and workflows: automate data ingestion, transformation and integration processes to streamline analytics operations and reduce manual effort.
Drive continuous improvement: identify opportunities for process enhancements, optimise data collection methods, design improved data solutions and recommend best practices.
Translate analytical outcomes into compelling presentations, written reports or executive summaries, communicating insights clearly to business, marketing, operations or senior leadership teams.
Monitor KPIs and performance metrics regularly, producing trend analysis and business‑impact reports to help steer organisational strategy.
Perform data validation, quality assurance checks and root cause analysis when discrepancies arise, and implement corrective actions and preventive measures.
Act as a liaison between data engineering, IT, operations and business teams to translate business questions into data insights and build effective analytics solutions.
Maintain detailed documentation for data structures, metadata, assumptions, definitions, metrics and analytic methodologies to ensure transparency and reproducibility.
Perform scenario modelling, ad‑hoc analyses or “what‑if” assessments to support strategic initiatives, product development or market research.
Benchmark data and performance against industry standards or competitive metrics, provide insight into market positioning and growth opportunities.
Support the improvement of analytical toolsets, recommend new analytics technologies, BI platforms or visualisation tools to enhance capability and efficiency.
Present insights to diverse audiences including technical teams, business managers and executive leadership, adapting communication style appropriately.
Stay current with emerging analytics, data sciences trends, data‑visualisation techniques and toolsets, and integrate learnings into the workflow.
Mentor or provide training to junior analysts, support knowledge sharing, encourage data‑driven culture across the organisation.
Assist with the preparation of data for regulatory reporting or external compliance submissions when required, particularly in data‑sensitive industries.

Secondary Functions

Support ad‑hoc data requests and exploratory data analysis for business units across marketing, operations or finance.
Contribute to the organisation’s data strategy and roadmap by offering insight into analytics capabilities, infrastructure and growth opportunities.
Collaborate with business units to translate their data needs into engineering or analytics requirements.
Participate in agile sprint planning, analytics backlog grooming and cross‑team ceremonies within the analytics or data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

Advanced proficiency with SQL and relational database management systems (e.g., MySQL, PostgreSQL, Oracle).
Strong programming/scripting skills in Python, R or similar for data wrangling, statistical analysis and automation.
Expertise in data visualisation tools and dashboards (e.g., Tableau, Power BI, Looker) to create interactive reports and insight‑driven displays.
Solid experience in cleaning, transforming and preparing large, complex data sets for analysis.
Strong statistical and analytical capability: regression analysis, hypothesis testing, trend identification and modelling.
Familiarity with Excel, advanced formulas, pivot tables, VBA or equivalent for ad‑hoc reporting.
Knowledge of database design, data warehousing, ETL frameworks and data architecture principles.
Understanding of data governance, data quality frameworks, data privacy regulations and compliance considerations.
Ability to work with BI or analytics platforms, cloud data services (BigQuery, Snowflake, Azure Synapse) and modern data pipelines.
Strong skills in presenting complex data to non‑technical stakeholders, creating executive summaries and explaining insight to drive decisions.

Soft Skills

Strong analytical thinking and problem‑solving mindset: ability to break down complex data challenges and propose actionable solutions.
Excellent verbal and written communication skills: capable of translating quantitative findings into business narratives.
Business acumen: understanding how data insights influence business strategy, operations, marketing and finance.
Collaboration and stakeholder management: working with cross‑functional teams (product, marketing, operations, engineering) to deliver insights.
Attention to detail and a high level of accuracy in data handling, reporting and documentation.
Curiosity and continuous learning orientation: staying current with analytics technologies and methods and applying innovation.
Time management and prioritisation skills: balancing multiple analyses, urgent ad‑hoc requests and longer‑term analytics projects.
Adaptability and flexibility: can work in fast‑paced environments with changing priorities, emerging data needs and evolving tools.
Mentorship and coaching mindset: able to support less experienced team members, drive best‑practice analytics culture.
Storytelling through data: able to craft a compelling narrative from data, influence decisions and present findings with clarity.

Education & Experience

Educational Background

Minimum Education:

Bachelor’s degree in Computer Science, Statistics, Mathematics, Data Science, Economics or a related field.

Preferred Education:

Master’s degree or certification in Data Analytics, Business Intelligence, Applied Statistics, or related discipline.

Relevant Fields of Study:

Computer Science
Statistics or Applied Mathematics
Data Science / Analytics
Information Systems / Business Analytics

Experience Requirements

Typical Experience Range:

2 to 4 years of experience as a Data Analyst or similar quantitative/analytics role.

Preferred:

4+ years of hands‑on experience analysing complex data sets, building dashboards and delivering business insights, preferably in a specific domain (e.g., marketing, operations, finance) with autonomy and stakeholder engagement.