Key Responsibilities and Required Skills for Data Business Analyst

🎯 Role Definition

The Data Business Analyst is responsible for bridging business strategy and data engineering by translating stakeholder requirements into analytical solutions, designing and maintaining enterprise-grade dashboards and reports, and delivering actionable insights that drive revenue, reduce cost, and improve operational efficiency. This role combines hands-on data analysis (SQL, Python/R), business acumen (metrics definition, ROI analysis), and stakeholder management to ensure analytics are accurate, accessible, and aligned with organizational goals. The ideal candidate is fluent in data visualization, KPI design, A/B test analysis, data governance best practices, and is comfortable working in Agile environments with data warehouses (e.g., Snowflake, BigQuery) and ETL/dbt pipelines.

📈 Career Progression

Typical Career Path

Entry Point From:

Junior Data Analyst / Reporting Analyst
Business Analyst with strong analytics exposure
Product Analyst or Operations Analyst

Advancement To:

Senior Data Business Analyst / Lead Data Analyst
Analytics Manager / Business Intelligence Manager
Product Analytics Manager or Data Product Owner

Lateral Moves:

Data Scientist (with additional modeling experience)
Data Engineer (with stronger technical pipeline skills)
Product Manager (data-focused)

Core Responsibilities

Primary Functions

Own end-to-end analytics deliverables by gathering business requirements, designing data models, writing performant SQL queries, and building production dashboards and reports that support executive, product, and operational decisions.
Translate ambiguous business problems into structured analytical plans, define hypotheses, identify data sources, design experiments or analyses, and present clear, prioritized recommendations that include expected business impact and measurable KPIs.
Design, implement, and maintain company-level KPIs and metric definitions in a centralized metric registry, ensuring consistency across dashboards, marketing, finance, and product reporting.
Build and maintain interactive dashboards and visualizations in Power BI, Tableau, Looker, or similar BI tools to surface trends, risks, and opportunities for stakeholders at all levels.
Partner with data engineering to design and validate ETL/ELT pipelines, data warehouse schemas (star/snowflake), and dbt models to ensure reliable, performant analytics-ready datasets.
Perform complex SQL-based analysis to support pricing, revenue forecasting, churn analysis, cohort analysis, funnel optimization, and customer segmentation initiatives that directly influence roadmaps and GTM strategy.
Lead ad hoc analyses and deep-dive investigations into anomalous metrics (e.g., traffic drops, conversion declines), producing root cause assessments and prioritized remediation recommendations.
Design and analyze A/B tests and experimentation results, including experiment design, power calculations, metric selection, and interpretation of statistically significant outcomes to inform product decisions.
Write clear, reproducible analytical documentation, playbooks, and runbooks (including data lineage, transformation logic, and dashboard usage guides) to improve team scalability and data literacy.
Work closely with Finance and Operations to develop financial models, build revenue and cost forecasts, produce monthly/quarterly reports, and ensure alignment between analytic outputs and business planning cycles.
Act as a subject matter expert in customer and product analytics by synthesizing user behavior data, product telemetry, and market data to recommend feature prioritization and roadmap changes.
Lead cross-functional analytic initiatives (e.g., pricing strategy, retention programs, supply optimization) by scoping work, coordinating stakeholders, estimating effort, and tracking measurable outcomes post-implementation.
Implement and enforce data quality monitoring, alerting, and reconciliation procedures; proactively identify gaps in data collection and collaborate with engineering to remediate instrumentation and schema issues.
Create and present executive-grade slide decks and storytelling narratives that communicate complex analytical findings, trade-offs, and suggested next steps to product leaders, marketing, and C-level audiences.
Influence product and business strategy through quantitative business cases that quantify forecasted ROI, cost-benefit, and sensitivity analyses to support investment prioritization.
Maintain up-to-date knowledge of analytics and BI best practices, competitive analytics approaches, and relevant tools to continuously improve team processes and tooling decisions.
Facilitate requirement workshops, user story definition, and acceptance criteria with product managers and engineers to ensure analytics needs are translated into reliable features and telemetry.
Drive adoption of self-service analytics by creating templates, standard views, and training sessions that empower non-technical stakeholders while safeguarding metric consistency.
Conduct supplier, partner, and vendor analytics assessments (e.g., third-party data integration, attribution vendors), validate vendor data, and make recommendations on vendor suitability and integration strategies.
Ensure compliance with data governance, privacy, and security policies during data access, reporting, and model development, partnering with Legal and Security teams as needed.
Mentor junior analysts by providing code reviews, analytical rigor, and feedback on dashboards, SQL, and storytelling to elevate team output quality.
Continuously iterate on analytics processes by setting KPI targets for the analytics function, tracking team SLAs (delivery time, data quality), and implementing process improvements.

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 evaluations for BI and analytics platforms and pilot new tools to improve reporting efficiency.
Provide training sessions and office hours to increase cross-functional data literacy and encourage self-service analytics.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL (window functions, CTEs, query optimization) for data extraction and transformation.
BI tools: proficient in Power BI, Tableau, Looker, or Looker Studio for dashboarding and visualization best practices.
Data modeling and warehousing experience (Star Schema, Snowflake schema) with platforms such as Snowflake, BigQuery, Redshift, or Azure Synapse.
Experience with ETL/ELT tools and frameworks (dbt, Airflow, Matillion) and familiarity with data pipeline testing and version control.
Scripting for analysis and automation in Python or R (pandas, numpy, statsmodels) for advanced analytics and reproducible processes.
Knowledge of statistical methods and experiment design (A/B testing, hypothesis testing, confidence intervals, power analysis).
Strong Excel skills including advanced formulas, pivot tables, Power Query, and financial modeling techniques.
Experience building and maintaining metric stores or centralized metric registries and implementing data governance patterns.
Familiarity with product analytics and event instrumentation (Mixpanel, Amplitude, Segment) and translating event taxonomies into actionable metrics.
Experience with cloud platforms and SQL dialects (Snowflake SQL, BigQuery SQL) and query performance tuning.
Basic familiarity with machine learning concepts, model evaluation metrics, and ability to partner with Data Scientists to productize models.
Comfortable using collaboration and project management tools such as Jira, Confluence, and Git/GitHub for reproducible analytics workflows.
Knowledge of data privacy and compliance (GDPR, CCPA) best practices as they relate to analytics and reporting.

Soft Skills

Strong stakeholder management and influencing skills — able to balance competing priorities and build consensus across product, finance, and operations.
Excellent written and verbal communication; capable of converting complex analyses into clear, actionable executive narratives.
Critical thinking and problem-solving mindset with a bias for measurable outcomes and iterative learning.
Attention to detail and ownership — ensures accuracy of metrics and accountability for delivery timelines.
Teaching and mentorship capability to elevate team analytics maturity and promote best practices.
Adaptability in fast-paced, ambiguous environments; able to scope and pivot analyses as business needs change.
Project management and prioritization skills to lead multi-stakeholder analytic projects from discovery to delivery.
Collaborative team player who values diverse perspectives and open communication.
Curiosity and continuous learning orientation to keep up with analytics tooling and industry trends.
Ethical judgment and responsibility when handling sensitive data and insights.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Business, Economics, Statistics, Computer Science, Data Science, Math, Engineering, or related quantitative field.

Preferred Education:

Master's degree in Business Analytics, Data Science, Statistics, MBA, or related advanced degree.
Professional certifications such as CBIP, Google Data Analytics Professional Certificate, Tableau/Power BI certifications are a plus.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range:

3–7+ years of professional analytics or business intelligence experience, depending on seniority.

Preferred:

5+ years if targeting senior or lead-level responsibilities with demonstrated ownership of enterprise dashboards, metric governance, and cross-functional analytic programs.
Proven track record of translating analytics into measurable business outcomes (e.g., improved retention, decreased churn, increased revenue, operational cost savings).
Experience in SaaS, e-commerce, fintech, or enterprise product analytics environments is highly desirable.