This article outlines the core responsibilities and requirements for a data-focused role, covering tasks from collecting and cleaning large datasets to delivering dashboards and insights. You’ll learn how exploratory data analysis, collaboration with engineers, A/B testing support, and automation feed decision-making, plus the technical skills—Python, SQL, visualization tools, and documentation—needed to execute these activities effectively. The following sections dive into responsibilities and required competencies.
Core Responsibilities and Workflow
The role centers on end-to-end handling of data: collect, clean, and structure large datasets from internal and external sources, then transform raw inputs into accurate, reliable information for analysis and reporting. Working closely with data engineers and developers improves data quality and reliability while documentation preserves dataset schemas and data flows.
- Data preparation: Clean and preprocess raw data using Python, SQL, or analytical tools; ensure accuracy, consistency, and security across systems; document datasets and analysis results.
- Exploration and insight: Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies that inform business and product teams and support leadership decision-making.
- Dashboards and reporting: Build and maintain dashboards in tools like Power BI, Tableau, or Looker Studio; contribute to reports used by leadership and prepare visualizations that simplify complex information for non-technical stakeholders.
- Testing and metrics: Support A/B testing, user behavior analysis, and performance metrics tracking; analyze KPIs such as user engagement, retention, and campaign performance to answer business questions.
- Operational efficiency: Automate repetitive data tasks (reports, daily metrics) and write efficient SQL queries to extract insights; participate in regular standups, sprint planning, and documentation tasks to align with cross-functional teams.
Required Skills, Tools, and Best Practices
Successful execution requires both technical proficiency and analytical rigor. The following skills and tools map directly to the responsibilities above and enable consistent, reproducible outcomes.
- Programming & data manipulation: Strong knowledge of Python (Pandas, NumPy) or R for data manipulation and cleaning; use these skills to preprocess raw data and perform EDA.
- SQL & databases: Hands-on SQL experience (PostgreSQL preferred) to write efficient queries that answer business questions and feed dashboards.
- Statistics & analytics: Understanding of statistics, probability, and analytical concepts to interpret EDA results, support A/B testing, and analyze KPIs.
- Visualization & reporting tools: Ability to create charts, dashboards, and reports using Matplotlib, Seaborn, Plotly, Power BI, Tableau, or Looker Studio; good Excel/Google Sheets skills for ad-hoc analysis.
- Data engineering concepts: Knowledge of ETL pipelines, data warehousing concepts, familiarity with APIs and JSON data structures, and collaboration with engineers to improve data pipelines.
- Process & collaboration: Experience with Git, version control, JIRA/Notion, or Agile methodologies; participate in standups and sprint planning to keep work aligned and documented.
- Analytical mindset: Ability to translate data outcomes into actionable insights, strong attention to detail, problem-solving skills, and a basic understanding of machine learning concepts (regression, clustering, classification) to complement analytical tasks.
In summary, this role blends hands-on data work — collecting, cleaning, EDA, dashboarding, A/B testing, and automation — with cross-functional collaboration to drive leadership decisions. Candidates must be proficient in Python or R, SQL, visualization tools, ETL concepts, and documentation while maintaining data accuracy and security. Strong analytical thinking, attention to detail, and familiarity with Agile workflows complete the profile.
