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Key Responsibilities and Required Skills for a Job Data Analyst

💰 $75,000 - $115,000

Data & AnalyticsBusiness IntelligenceTechnology

🎯 Role Definition

This role requires a highly motivated and detail-oriented Job Data Analyst to join our dynamic team. In this pivotal role, you will be the bridge between raw data and strategic business outcomes. You will dive deep into complex datasets, uncover hidden trends, and translate your findings into compelling stories and actionable insights. The ideal candidate is a curious problem-solver with a passion for data, who thrives in a collaborative environment and is eager to help stakeholders make smarter, data-informed decisions that will shape the future of our business.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst / Analytics Intern
  • Business Analyst
  • Data Coordinator / Data Entry Specialist

Advancement To:

  • Senior Data Analyst
  • Analytics Manager / Team Lead
  • Data Scientist

Lateral Moves:

  • Business Intelligence (BI) Developer
  • Data Engineer

Core Responsibilities

Primary Functions

  • Design, develop, and maintain robust data models, reporting systems, data automation systems, dashboards, and performance metrics that support key business decisions.
  • Conduct in-depth analysis of large, complex datasets to identify trends, patterns, and anomalies, translating findings into actionable recommendations for business stakeholders.
  • Acquire data from primary and secondary sources, and develop processes for maintaining and cleansing databases and data systems to ensure accuracy and integrity.
  • Author and optimize complex SQL queries to extract, manipulate, and aggregate data from relational databases like PostgreSQL, MySQL, or SQL Server.
  • Utilize statistical methods and data analysis techniques to interpret results and provide ongoing reports on business performance and key initiatives.
  • Create compelling data visualizations and interactive dashboards using BI tools (e.g., Tableau, Power BI, Looker) to communicate complex data stories effectively to non-technical audiences.
  • Collaborate with cross-functional teams including product, marketing, finance, and engineering to understand their data needs and deliver relevant analytical solutions.
  • Develop and implement A/B testing frameworks and analyze test results to drive product and marketing optimization.
  • Perform root cause analysis to investigate and identify the source of discrepancies or unexpected trends in data.
  • Build and maintain data dictionaries and documentation for key datasets and reports to ensure consistency and understanding across the organization.
  • Automate recurring data analysis and reporting tasks using scripting languages like Python or R to improve efficiency and scalability.
  • Monitor and analyze key performance indicators (KPIs), providing regular updates and insights to senior leadership.
  • Translate business requirements into technical specifications for data warehousing and ETL/ELT processes.
  • Proactively identify opportunities for process improvements and new analytical projects that can drive significant business value.
  • Prepare and present clear, concise analytical findings and recommendations to diverse audiences, from technical peers to executive leadership.
  • Perform market and customer segmentation analysis to identify target audiences and personalize user experiences.
  • Develop predictive models to forecast future trends, customer behavior, and business outcomes.
  • Ensure compliance with data governance and security policies throughout the entire data lifecycle.
  • Mentor junior analysts and contribute to the development of a strong data-driven culture within the company.
  • Evaluate and recommend new technologies and tools to enhance the organization's analytical capabilities.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis from various business units.
  • Contribute to the organization's data strategy and long-term roadmap.
  • Collaborate with business units to translate functional data needs into technical requirements for the data engineering team.
  • Participate in sprint planning, stand-ups, and other agile ceremonies within the analytics and data teams.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL: High proficiency in writing complex, efficient SQL queries, including window functions, CTEs, and joins across various database systems (e.g., PostgreSQL, BigQuery, Snowflake).
  • Business Intelligence Tools: Hands-on experience creating dashboards and reports in BI platforms like Tableau, Microsoft Power BI, Looker, or Qlik.
  • Programming for Data Analysis: Strong skills in Python (with libraries like Pandas, NumPy, Scikit-learn) or R for data manipulation, statistical analysis, and automation.
  • Advanced Spreadsheet Skills: Mastery of Microsoft Excel or Google Sheets, including pivot tables, advanced formulas, VLOOKUP/INDEX-MATCH, and data modeling.
  • Statistical Knowledge: Solid understanding of fundamental statistical concepts and techniques (e.g., hypothesis testing, regression analysis, distributions).
  • Data Warehousing Concepts: Familiarity with data warehouse architecture, ETL/ELT processes, and dimensional modeling (star/snowflake schemas).
  • Database Management: Experience working directly with relational and non-relational databases.
  • Data Visualization: A strong eye for design and the ability to tell a clear, compelling story with data through effective charts and graphs.
  • Version Control: Familiarity with Git for collaborating on code and managing analytical projects.
  • Cloud Platform Exposure: Experience with data services on cloud platforms like AWS (S3, Redshift), Google Cloud (BigQuery), or Azure is a plus.

Soft Skills

  • Analytical and Critical Thinking: Ability to deconstruct complex problems, analyze data from multiple angles, and draw logical, data-driven conclusions.
  • Communication & Storytelling: Excellent verbal and written communication skills, with a knack for translating technical findings into clear, impactful narratives for non-technical stakeholders.
  • Attention to Detail: Meticulous approach to data validation, cleansing, and analysis to ensure the highest level of accuracy and quality.
  • Problem-Solving: A proactive and resourceful approach to overcoming challenges and finding innovative solutions.
  • Business Acumen: A strong understanding of business operations and the ability to connect data insights to strategic business goals.
  • Collaboration & Teamwork: Proven ability to work effectively within cross-functional teams to achieve common objectives.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in a quantitative or related field.

Preferred Education:

  • Master's degree in a quantitative field.

Relevant Fields of Study:

  • Computer Science, Statistics, Mathematics
  • Economics, Business Analytics, Information Systems

Experience Requirements

Typical Experience Range:

  • 2-5 years of direct experience in a data analyst, business intelligence analyst, or similar role.

Preferred:

  • Experience in a fast-paced, data-rich environment (e.g., tech, e-commerce, finance, or consulting).
  • A portfolio of projects (e.g., on GitHub or a personal website) showcasing your analytical skills and data visualization work is highly desirable.
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